id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1310.8388 | Provable Security of Networks | cs.SI physics.soc-ph | We propose a definition of {\it security} and a definition of {\it
robustness} of networks against the cascading failure models of deliberate
attacks and random errors respectively, and investigate the principles of the
security and robustness of networks. We propose a {\it security model} such
that networks constructed by the model are provably secure against any attacks
of small sizes under the cascading failure models, and simultaneously follow a
power law, and have the small world property with a navigating algorithm of
time complex $O(\log n)$. It is shown that for any network $G$ constructed from
the security model, $G$ satisfies some remarkable topological properties,
including: (i) the {\it small community phenomenon}, that is, $G$ is rich in
communities of the form $X$ of size poly logarithmic in $\log n$ with
conductance bounded by $O(\frac{1}{|X|^{\beta}})$ for some constant $\beta$,
(ii) small diameter property, with diameter $O(\log n)$ allowing a navigation
by a $O(\log n)$ time algorithm to find a path for arbitrarily given two nodes,
and (iii) power law distribution, and satisfies some probabilistic and
combinatorial principles, including the {\it degree priority theorem}, and {\it
infection-inclusion theorem}. By using these principles, we show that a network
$G$ constructed from the security model is secure for any attacks of small
scales under both the uniform threshold and random threshold cascading failure
models. Our security theorems show that networks constructed from the security
model are provably secure against any attacks of small sizes, for which natural
selections of {\it homophyly, randomness} and {\it preferential attachment} are
the underlying mechanisms.
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1310.8390 | Harnack's inequality and Green functions on locally finite graphs | math.DG cs.IT math.AP math.CA math.IT math.MG | In this paper we study the gradient estimate for positive solutions of
Schrodinger equations on locally finite graph. Then we derive Harnack's
inequality for positive solutions of the Schrodinger equations. We also set up
some results about Green functions of the Laplacian equation on locally finite
graph. Interesting properties of Schrodinger equation are derived.
|
1310.8396 | Tunable and Growing Network Generation Model with Community Structures | cs.SI physics.soc-ph | Recent years have seen a growing interest in the modeling and simulation of
social networks to understand several social phenomena. Two important classes
of networks, small world and scale free networks have gained a lot of research
interest. Another important characteristic of social networks is the presence
of community structures. Many social processes such as information diffusion
and disease epidemics depend on the presence of community structures making it
an important property for network generation models to be incorporated. In this
paper, we present a tunable and growing network generation model with small
world and scale free properties as well as the presence of community
structures. The major contribution of this model is that the communities thus
created satisfy three important structural properties: connectivity within each
community follows power-law, communities have high clustering coefficient and
hierarchical community structures are present in the networks generated using
the proposed model. Furthermore, the model is highly robust and capable of
producing networks with a number of different topological characteristics
varying clustering coefficient and inter-cluster edges. Our simulation results
show that the model produces small world and scale free networks along with the
presence of communities depicting real world societies and social networks.
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1310.8418 | An efficient distributed learning algorithm based on effective local
functional approximations | cs.LG | Scalable machine learning over big data is an important problem that is
receiving a lot of attention in recent years. On popular distributed
environments such as Hadoop running on a cluster of commodity machines,
communication costs are substantial and algorithms need to be designed suitably
considering those costs. In this paper we give a novel approach to the
distributed training of linear classifiers (involving smooth losses and L2
regularization) that is designed to reduce the total communication costs. At
each iteration, the nodes minimize locally formed approximate objective
functions; then the resulting minimizers are combined to form a descent
direction to move. Our approach gives a lot of freedom in the formation of the
approximate objective function as well as in the choice of methods to solve
them. The method is shown to have $O(log(1/\epsilon))$ time convergence. The
method can be viewed as an iterative parameter mixing method. A special
instantiation yields a parallel stochastic gradient descent method with strong
convergence. When communication times between nodes are large, our method is
much faster than the Terascale method (Agarwal et al., 2011), which is a state
of the art distributed solver based on the statistical query model (Chuet al.,
2006) that computes function and gradient values in a distributed fashion. We
also evaluate against other recent distributed methods and demonstrate superior
performance of our method.
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1310.8428 | Multilabel Classification through Random Graph Ensembles | cs.LG | We present new methods for multilabel classification, relying on ensemble
learning on a collection of random output graphs imposed on the multilabel and
a kernel-based structured output learner as the base classifier. For ensemble
learning, differences among the output graphs provide the required base
classifier diversity and lead to improved performance in the increasing size of
the ensemble. We study different methods of forming the ensemble prediction,
including majority voting and two methods that perform inferences over the
graph structures before or after combining the base models into the ensemble.
We compare the methods against the state-of-the-art machine learning approaches
on a set of heterogeneous multilabel benchmark problems, including multilabel
AdaBoost, convex multitask feature learning, as well as single target learning
approaches represented by Bagging and SVM. In our experiments, the random graph
ensembles are very competitive and robust, ranking first or second on most of
the datasets. Overall, our results show that random graph ensembles are viable
alternatives to flat multilabel and multitask learners.
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1310.8462 | Application of Data Mining In Marketing | cs.DB cs.CY | One of the most important problems in modern finance is finding efficient
ways to summarize and visualize the stock market data to give individuals or
institutions useful information about the market behavior for investment
decisions. The enormous amount of valuable data generated by the stock market
has attracted researchers to explore this problem domain using different
methodologies. Potential significant benefits of solving these problems
motivated extensive research for years. The research in data mining has gained
a high attraction due to the importance of its applications and the increasing
generation information. This paper provides an overview of application of data
mining techniques such as decision tree. Also, this paper reveals progressive
applications in addition to existing gap and less considered area and
determines the future works for researchers.
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1310.8467 | Reinforcement Learning Framework for Opportunistic Routing in WSNs | cs.NI cs.LG | Routing packets opportunistically is an essential part of multihop ad hoc
wireless sensor networks. The existing routing techniques are not adaptive
opportunistic. In this paper we have proposed an adaptive opportunistic routing
scheme that routes packets opportunistically in order to ensure that packet
loss is avoided. Learning and routing are combined in the framework that
explores the optimal routing possibilities. In this paper we implemented this
Reinforced learning framework using a customer simulator. The experimental
results revealed that the scheme is able to exploit the opportunistic to
optimize routing of packets even though the network structure is unknown.
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1310.8468 | Sparse Signal Recovery from Nonadaptive Linear Measurements | cs.IT math.IT | The theory of Compressed Sensing, the emerging sampling paradigm 'that goes
against the common wisdom', asserts that 'one can recover signals in Rn from
far fewer samples or measurements, if the signal has a sparse representation in
some orthonormal basis', from m = O(klogn), k<< n nonadaptive measurements .
The accuracy of the recovered signal is 'as good as that attainable with direct
knowledge of the k most important coefficients and its locations'. Moreover, a
good approximation to those important coefficients is extracted from the
measurements by solving a L1 minimization problem viz. Basis Pursuit. 'The
nonadaptive measurements have the character of random linear combinations of
the basis/frame elements'.
The theory has implications which are far reaching and immediately leads to a
number of applications in Data Compression,Channel Coding and Data Acquisition.
'The last of these applications suggest that CS could have an enormous impact
in areas where conventional hardware design has significant limitations',
leading to 'efficient and revolutionary methods of data acquisition and storage
in future'.
The paper reviews fundamental mathematical ideas pertaining to compressed
sensing viz. sparsity, incoherence, reduced isometry property and basis
pursuit, exemplified by the sparse recovery of a speech signal and convergence
of the L1- minimization algorithm.
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1310.8487 | Information Loss and Anti-Aliasing Filters in Multirate Systems | cs.IT math.IT | This work investigates the information loss in a decimation system, i.e., in
a downsampler preceded by an anti-aliasing filter. It is shown that, without a
specific signal model in mind, the anti-aliasing filter cannot reduce
information loss, while, e.g., for a simple signal-plus-noise model it can. For
the Gaussian case, the optimal anti-aliasing filter is shown to coincide with
the one obtained from energetic considerations. For a non-Gaussian signal
corrupted by Gaussian noise, the Gaussian assumption yields an upper bound on
the information loss, justifying filter design principles based on second-order
statistics from an information-theoretic point-of-view.
|
1310.8499 | Deep AutoRegressive Networks | cs.LG stat.ML | We introduce a deep, generative autoencoder capable of learning hierarchies
of distributed representations from data. Successive deep stochastic hidden
layers are equipped with autoregressive connections, which enable the model to
be sampled from quickly and exactly via ancestral sampling. We derive an
efficient approximate parameter estimation method based on the minimum
description length (MDL) principle, which can be seen as maximising a
variational lower bound on the log-likelihood, with a feedforward neural
network implementing approximate inference. We demonstrate state-of-the-art
generative performance on a number of classic data sets: several UCI data sets,
MNIST and Atari 2600 games.
|
1310.8508 | The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia
coverage of academics | physics.soc-ph cs.CY cs.DL cs.SI physics.data-an | Activity of modern scholarship creates online footprints galore. Along with
traditional metrics of research quality, such as citation counts, online images
of researchers and institutions increasingly matter in evaluating academic
impact, decisions about grant allocation, and promotion. We examined 400
biographical Wikipedia articles on academics from four scientific fields to
test if being featured in the world's largest online encyclopedia is correlated
with higher academic notability (assessed through citation counts). We found no
statistically significant correlation between Wikipedia articles metrics
(length, number of edits, number of incoming links from other articles, etc.)
and academic notability of the mentioned researchers. We also did not find any
evidence that the scientists with better WP representation are necessarily more
prominent in their fields. In addition, we inspected the Wikipedia coverage of
notable scientists sampled from Thomson Reuters list of "highly cited
researchers". In each of the examined fields, Wikipedia failed in covering
notable scholars properly. Both findings imply that Wikipedia might be
producing an inaccurate image of academics on the front end of science. By
shedding light on how public perception of academic progress is formed, this
study alerts that a subjective element might have been introduced into the
hitherto structured system of academic evaluation.
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1310.8509 | Construction of extremal or optimal codes with an automorphism of order
29 | cs.IT math.IT | In this paper we construct a new optimal code with parameters [120, 60, 20]
of type II with an automorphism of order 29. Furthermore we classify all
extremal codes with length 60 of type I with an automorphism of this order.
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1310.8511 | A Preadapted Universal Switch Distribution for Testing Hilberg's
Conjecture | cs.IT cs.CL math.IT | Hilberg's conjecture about natural language states that the mutual
information between two adjacent long blocks of text grows like a power of the
block length. The exponent in this statement can be upper bounded using the
pointwise mutual information estimate computed for a carefully chosen code. The
bound is the better, the lower the compression rate is but there is a
requirement that the code be universal. So as to improve a received upper bound
for Hilberg's exponent, in this paper, we introduce two novel universal codes,
called the plain switch distribution and the preadapted switch distribution.
Generally speaking, switch distributions are certain mixtures of adaptive
Markov chains of varying orders with some additional communication to avoid so
called catch-up phenomenon. The advantage of these distributions is that they
both achieve a low compression rate and are guaranteed to be universal. Using
the switch distributions we obtain that a sample of a text in English is
non-Markovian with Hilberg's exponent being $\le 0.83$, which improves over the
previous bound $\le 0.94$ obtained using the Lempel-Ziv code.
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1310.8532 | On the Capacity of Multiple Access and Broadcast Fading Channels with
Full Channel State Information at Low SNR | cs.IT math.IT | We study the throughput capacity region of the Gaussian multi-access (MAC)
fading channel with perfect channel state information (CSI) at the receiver and
at the transmitters, at low power regime. We show that it has a
multidimensional rectangle structure and thus is simply characterized by single
user capacity points. More specifically, we show that at low power regime, the
boundary surface of the capacity region shrinks to a single point corresponding
to the sum rate maximizer and that the coordinates of this point coincide with
single user capacity bounds. Inspired by this result, we propose an on-off
scheme, compute its achievable rate, and show that this scheme achieves single
user capacity bounds of the MAC channel for a wide class of fading channels at
asymptotically low power regime. We argue that this class of fading encompasses
all known wireless channels for which the capacity region of the MAC channel
has even a simpler expression in terms of users' average power constraints
only. Using the duality of Gaussian MAC and broadcast channels (BC), we deduce
a simple characterization of the BC capacity region at low power regime and
show that for a class of fading channels (including Rayleigh fading),
time-sharing is asymptotically optimal.
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1310.8540 | Quantitative Assessment of TV White Space in India | cs.IT math.IT | Licensed but unutilized television (TV) band spectrum is called as TV white
space in the literature. Ultra high frequency (UHF) TV band spectrum has very
good wireless radio propagation characteristics. The amount of TV white space
in the UHF TV band in India is of interest. Comprehensive quantitative
assessment and estimates for the TV white space in the 470-590MHz band for four
zones of India (all except north) are presented in this work. This is the first
effort in India to estimate TV white spaces in a comprehensive manner. The
average available TV white space per unit area in these four zones is
calculated using two methods: (i) the primary (licensed) user and secondary
(unlicensed) user point of view; and, (ii) the regulations of Federal
Communications Commission in the United States. By both methods, the average
available TV white space in the UHF TV band is shown to be more than 100MHz! A
TV transmitter frequency-reassignment algorithm is also described. Based on
spatial-reuse ideas, a TV channel allocation scheme is presented which results
in insignicant interference to the TV receivers while using the least number of
TV channels for transmission across the four zones. Based on this reassignment,
it is found that four TV band channels (or 32MHz) are sufficient to provide the
existing UHF TV band coverage in India.
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1310.8583 | A Hybrid Local Search for Simplified Protein Structure Prediction | cs.CE cs.AI | Protein structure prediction based on Hydrophobic-Polar energy model
essentially becomes searching for a conformation having a compact hydrophobic
core at the center. The hydrophobic core minimizes the interaction energy
between the amino acids of the given protein. Local search algorithms can
quickly find very good conformations by moving repeatedly from the current
solution to its "best" neighbor. However, once such a compact hydrophobic core
is found, the search stagnates and spends enormous effort in quest of an
alternative core. In this paper, we attempt to restructure segments of a
conformation with such compact core. We select one large segment or a number of
small segments and apply exhaustive local search. We also apply a mix of
heuristics so that one heuristic can help escape local minima of another. We
evaluated our algorithm by using Face Centered Cubic (FCC) Lattice on a set of
standard benchmark proteins and obtain significantly better results than that
of the state-of-the-art methods.
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1310.8588 | A Meta-heuristically Approach of the Spatial Assignment Problem of Human
Resources in Multi-sites Enterprise | cs.AI | The aim of this work is to present a meta-heuristically approach of the
spatial assignment problem of human resources in multi-sites enterprise.
Usually, this problem consists to move employees from one site to another based
on one or more criteria. Our goal in this new approach is to improve the
quality of service and performance of all sites with maximizing an objective
function under some managers imposed constraints. The formulation presented
here of this problem coincides perfectly with a Combinatorial Optimization
Problem (COP) which is in the most cases NP-hard to solve optimally. To avoid
this difficulty, we have opted to use a meta-heuristic popular method, which is
the genetic algorithm, to solve this problem in concrete cases. The results
obtained have shown the effectiveness of our approach, which remains until now
very costly in time. But the reduction of the time can be obtained by different
ways that we plan to do in the next work.
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1310.8599 | Information Compression, Intelligence, Computing, and Mathematics | cs.AI | This paper presents evidence for the idea that much of artificial
intelligence, human perception and cognition, mainstream computing, and
mathematics, may be understood as compression of information via the matching
and unification of patterns. This is the basis for the "SP theory of
intelligence", outlined in the paper and fully described elsewhere. Relevant
evidence may be seen: in empirical support for the SP theory; in some
advantages of information compression (IC) in terms of biology and engineering;
in our use of shorthands and ordinary words in language; in how we merge
successive views of any one thing; in visual recognition; in binocular vision;
in visual adaptation; in how we learn lexical and grammatical structures in
language; and in perceptual constancies. IC via the matching and unification of
patterns may be seen in both computing and mathematics: in IC via equations; in
the matching and unification of names; in the reduction or removal of
redundancy from unary numbers; in the workings of Post's Canonical System and
the transition function in the Universal Turing Machine; in the way computers
retrieve information from memory; in systems like Prolog; and in the
query-by-example technique for information retrieval. The chunking-with-codes
technique for IC may be seen in the use of named functions to avoid repetition
of computer code. The schema-plus-correction technique may be seen in functions
with parameters and in the use of classes in object-oriented programming. And
the run-length coding technique may be seen in multiplication, in division, and
in several other devices in mathematics and computing. The SP theory resolves
the apparent paradox of "decompression by compression". And computing and
cognition as IC is compatible with the uses of redundancy in such things as
backup copies to safeguard data and understanding speech in a noisy
environment.
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1310.8615 | Diffusion LMS for clustered multitask networks | cs.SY cs.IT cs.MA math.IT | Recent research works on distributed adaptive networks have intensively
studied the case where the nodes estimate a common parameter vector
collaboratively. However, there are many applications that are
multitask-oriented in the sense that there are multiple parameter vectors that
need to be inferred simultaneously. In this paper, we employ diffusion
strategies to develop distributed algorithms that address clustered multitask
problems by minimizing an appropriate mean-square error criterion with
$\ell_2$-regularization. Some results on the mean-square stability and
convergence of the algorithm are also provided. Simulations are conducted to
illustrate the theoretical findings.
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1310.8620 | Distributed Control of Networked Dynamical Systems: Static Feedback,
Integral Action and Consensus | math.DS cs.SY | This paper analyzes distributed control protocols for first- and second-order
networked dynamical systems. We propose a class of nonlinear consensus
controllers where the input of each agent can be written as a product of a
nonlinear gain, and a sum of nonlinear interaction functions. By using integral
Lyapunov functions, we prove the stability of the proposed control protocols,
and explicitly characterize the equilibrium set. We also propose a distributed
proportional-integral (PI) controller for networked dynamical systems. The PI
controllers successfully attenuate constant disturbances in the network. We
prove that agents with single-integrator dynamics are stable for any integral
gain, and give an explicit tight upper bound on the integral gain for when the
system is stable for agents with double-integrator dynamics. Throughout the
paper we highlight some possible applications of the proposed controllers by
realistic simulations of autonomous satellites, power systems and building
temperature control.
|
1311.0035 | Parameterless Optimal Approximate Message Passing | cs.IT math.IT math.ST stat.ML stat.TH | Iterative thresholding algorithms are well-suited for high-dimensional
problems in sparse recovery and compressive sensing. The performance of this
class of algorithms depends heavily on the tuning of certain threshold
parameters. In particular, both the final reconstruction error and the
convergence rate of the algorithm crucially rely on how the threshold parameter
is set at each step of the algorithm. In this paper, we propose a
parameter-free approximate message passing (AMP) algorithm that sets the
threshold parameter at each iteration in a fully automatic way without either
having an information about the signal to be reconstructed or needing any
tuning from the user. We show that the proposed method attains both the minimum
reconstruction error and the highest convergence rate. Our method is based on
applying the Stein unbiased risk estimate (SURE) along with a modified gradient
descent to find the optimal threshold in each iteration. Motivated by the
connections between AMP and LASSO, it could be employed to find the solution of
the LASSO for the optimal regularization parameter. To the best of our
knowledge, this is the first work concerning parameter tuning that obtains the
fastest convergence rate with theoretical guarantees.
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1311.0053 | Robust Compressed Sensing and Sparse Coding with the Difference Map | cs.CV physics.data-an stat.ML | In compressed sensing, we wish to reconstruct a sparse signal $x$ from
observed data $y$. In sparse coding, on the other hand, we wish to find a
representation of an observed signal $y$ as a sparse linear combination, with
coefficients $x$, of elements from an overcomplete dictionary. While many
algorithms are competitive at both problems when $x$ is very sparse, it can be
challenging to recover $x$ when it is less sparse. We present the Difference
Map, which excels at sparse recovery when sparseness is lower and noise is
higher. The Difference Map out-performs the state of the art with
reconstruction from random measurements and natural image reconstruction via
sparse coding.
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1311.0059 | Revisiting Aggregation for Data Intensive Applications: A Performance
Study | cs.DB | Aggregation has been an important operation since the early days of
relational databases. Today's Big Data applications bring further challenges
when processing aggregation queries, demanding adaptive aggregation algorithms
that can process large volumes of data relative to a potentially limited memory
budget (especially in multiuser settings). Despite its importance, the design
and evaluation of aggregation algorithms has not received the same attention
that other basic operators, such as joins, have received in the literature. As
a result, when considering which aggregation algorithm(s) to implement in a new
parallel Big Data processing platform (AsterixDB), we faced a lack of "off the
shelf" answers that we could simply read about and then implement based on
prior performance studies.
In this paper we revisit the engineering of efficient local aggregation
algorithms for use in Big Data platforms. We discuss the salient implementation
details of several candidate algorithms and present an in-depth experimental
performance study to guide future Big Data engine developers. We show that the
efficient implementation of the aggregation operator for a Big Data platform is
non-trivial and that many factors, including memory usage, spilling strategy,
and I/O and CPU cost, should be considered. Further, we introduce precise cost
models that can help in choosing an appropriate algorithm based on input
parameters including memory budget, grouping key cardinality, and data skew.
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1311.0090 | Conceptual quantification of the dynamicity of longitudinal social
networks | cs.SI physics.soc-ph | A longitudinal social network evolves over time through the creation and/ or
deletion of links among a set of actors (e.g. individuals or organizations).
Longitudinal social networks are studied by network science and social science
researchers to understand networke volution, trend propagation, friendship and
belief formation, diffusion of innovation, the spread of deviant behavior and
more. In the current literature, there are different approaches and methods
(e.g. Sampsons approach and the markov model) to study the dynamics of
longitudinal social networks. These approaches and methods have mainly been
utilised to explore evolutionary changes of longitudinal social networks from
one state to another and to explain the underlying reasons for these changes.
However, they cannot quantify the level of dynamicity of the over time network
changes and the contribution of individual network members (i.e. actors) to
these changes. In this study, we first develop a set of measures to quantify
different aspects of the dynamicity of a longitudinal social network. We then
apply these measures, in order to conduct empirical investigations, to two
different longitudinal social networks. Finally, we discuss the implications of
the application of these measures and possible future research directions of
this study.
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1311.0095 | Reconstruction algorithm in compressed sensing based on maximum a
posteriori estimation | cs.IT cond-mat.dis-nn math.IT | We propose a systematic method for constructing a sparse data reconstruction
algorithm in compressed sensing at a relatively low computational cost for
general observation matrix. It is known that the cost of l1-norm minimization
using a standard linear programming algorithm is O(N^3). We show that this cost
can be reduced to O(N^2) by applying the approach of posterior maximization.
Furthermore, in principle, the algorithm from our approach is expected to
achieve the widest successful reconstruction region, which is evaluated from
theoretical argument. We also discuss the relation between the belief
propagation-based reconstruction algorithm introduced in preceding works and
our approach.
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1311.0100 | An Efficient Feedback Coding Scheme with Low Error Probability for
Discrete Memoryless Channels | cs.IT math.IT | Existing fixed-length feedback communication schemes are either specialized
to particular channels (Schalkwijk--Kailath, Horstein), or apply to general
channels but either have high coding complexity (block feedback schemes) or are
difficult to analyze (posterior matching). This paper introduces a new
fixed-length feedback coding scheme which achieves the capacity for all
discrete memoryless channels, has an error exponent that approaches the sphere
packing bound as the rate approaches the capacity, and has $O(n\log n)$ coding
complexity. These benefits are achieved by judiciously combining features from
previous schemes with new randomization technique and encoding/decoding rule.
These new features make the analysis of the error probability for the new
scheme easier than for posterior matching.
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1311.0110 | Opportunistic Multiuser Two-Way Amplify-and-Forward Relaying with a
Multi Antenna Relay | cs.IT math.IT | We consider the opportunistic multiuser diversity in the multiuser two-way
amplify-and-forward (AF) relay channel. The relay, equipped with multiple
antennas and a simple zero-forcing beam-forming scheme, selects a set of two
way relaying user pairs to enhance the degree of freedom (DoF) and consequently
the sum throughput of the system. The proposed channel aligned pair scheduling
(CAPS) algorithm reduces the inter-pair interference and keeps the signal to
interference plus noise power ratio (SINR) of user pairs relatively
interference free in average sense when the number of user pairs become very
large. For ideal situations, where the number of user pairs grows faster than
the system signal to noise ratio (SNR), the DoF of $M$ per channel use can be
achieved when $M$ is the relay antenna size. With a limited number of pairs,
the system is overloaded and the sum rates saturate at high signal to noise
ratio (SNR) though modifications of CAPS can improve the performance to a
certain amount. The performance of CAPS can be further enhanced by
semi-orthogonal channel aligned pair scheduling (SCAPS) algorithm, which not
only aligns the pair channels but also forms semi-orthogonal inter-pair
channels. Simulation results show that we provide a set of approaches based on
(S)CAPS and modified (S)CAPS, which provides system performance benefit
depending on the SNR and the number of user pairs in the network.
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1311.0119 | Structure-preserving color transformations using Laplacian commutativity | cs.CV cs.GR math.SP | Mappings between color spaces are ubiquitous in image processing problems
such as gamut mapping, decolorization, and image optimization for color-blind
people. Simple color transformations often result in information loss and
ambiguities (for example, when mapping from RGB to grayscale), and one wishes
to find an image-specific transformation that would preserve as much as
possible the structure of the original image in the target color space. In this
paper, we propose Laplacian colormaps, a generic framework for
structure-preserving color transformations between images. We use the image
Laplacian to capture the structural information, and show that if the color
transformation between two images preserves the structure, the respective
Laplacians have similar eigenvectors, or in other words, are approximately
jointly diagonalizable. Employing the relation between joint diagonalizability
and commutativity of matrices, we use Laplacians commutativity as a criterion
of color mapping quality and minimize it w.r.t. the parameters of a color
transformation to achieve optimal structure preservation. We show numerous
applications of our approach, including color-to-gray conversion, gamut
mapping, multispectral image fusion, and image optimization for color deficient
viewers.
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1311.0121 | Subspace Thresholding Pursuit: A Reconstruction Algorithm for Compressed
Sensing | cs.IT math.IT | We propose a new iterative greedy algorithm for reconstructions of sparse
signals with or without noisy perturbations in compressed sensing. The proposed
algorithm, called \emph{subspace thresholding pursuit} (STP) in this paper, is
a simple combination of subspace pursuit and iterative hard thresholding.
Firstly, STP has the theoretical guarantee comparable to that of $\ell_1$
minimization in terms of restricted isometry property. Secondly, with a tuned
parameter, on the one hand, when reconstructing Gaussian signals, it can
outperform other state-of-the-art reconstruction algorithms greatly; on the
other hand, when reconstructing constant amplitude signals with random signs,
it can outperform other state-of-the-art iterative greedy algorithms and even
outperform $\ell_1$ minimization if the undersampling ratio is not very large.
In addition, we propose a simple but effective method to improve the empirical
performance further if the undersampling ratio is large. Finally, it is showed
that other iterative greedy algorithms can improve their empirical performance
by borrowing the idea of STP.
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1311.0124 | Reconstruction of Complex-Valued Fractional Brownian Motion Fields Based
on Compressive Sampling and Its Application to PSF Interpolation in Weak
Lensing Survey | cs.CV astro-ph.CO | A new reconstruction method of complex-valued fractional Brownian motion
(CV-fBm) field based on Compressive Sampling (CS) is proposed. The decay
property of Fourier coefficients magnitude of the fBm signals/ fields indicates
that fBms are compressible. Therefore, a few numbers of samples will be
sufficient for a CS based method to reconstruct the full field. The
effectiveness of the proposed method is showed by simulating, random sampling,
and reconstructing CV-fBm fields. Performance evaluation shows advantages of
the proposed method over boxcar filtering and thin plate methods. It is also
found that the reconstruction performance depends on both of the fBm's Hurst
parameter and the number of samples, which in fact is consistent with the CS
reconstruction theory. In contrast to other fBm or fractal interpolation
methods, the proposed CS based method does not require the knowledge of fractal
parameters in the reconstruction process; the inherent sparsity is just
sufficient for the CS to do the reconstruction. Potential applicability of the
proposed method in weak gravitational lensing survey, particularly for
interpolating non-smooth PSF (Point Spread Function) distribution representing
distortion by a turbulent field is also discussed.
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1311.0162 | Iterative Bilateral Filtering of Polarimetric SAR Data | cs.CV | In this paper, we introduce an iterative speckle filtering method for
polarimetric SAR (PolSAR) images based on the bilateral filter. To locally
adapt to the spatial structure of images, this filter relies on pixel
similarities in both spatial and radiometric domains. To deal with polarimetric
data, we study the use of similarities based on a statistical distance called
Kullback-Leibler divergence as well as two geodesic distances on Riemannian
manifolds. To cope with speckle, we propose to progressively refine the result
thanks to an iterative scheme. Experiments are run over synthetic and
experimental data. First, simulations are generated to study the effects of
filtering parameters in terms of polarimetric reconstruction error, edge
preservation and smoothing of homogeneous areas. Comparison with other methods
shows that our approach compares well to other state of the art methods in the
extraction of polarimetric information and shows superior performance for edge
restoration and noise smoothing. The filter is then applied to experimental
data sets from ESAR and FSAR sensors (DLR) at L-band and S-band, respectively.
These last experiments show the ability of the filter to restore structures
such as buildings and roads and to preserve boundaries between regions while
achieving a high amount of smoothing in homogeneous areas.
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1311.0181 | The Log-Volume of Optimal Codes for Memoryless Channels, Asymptotically
Within A Few Nats | cs.IT math.IT | Shannon's analysis of the fundamental capacity limits for memoryless
communication channels has been refined over time. In this paper, the maximum
volume $M_\avg^*(n,\epsilon)$ of length-$n$ codes subject to an average
decoding error probability $\epsilon$ is shown to satisfy the following tight
asymptotic lower and upper bounds as $n \to \infty$: \[ \underline{A}_\epsilon
+ o(1) \le \log M_\avg^*(n,\epsilon) - [nC - \sqrt{nV_\epsilon}
\,Q^{-1}(\epsilon) + \frac{1}{2} \log n] \le \overline{A}_\epsilon + o(1) \]
where $C$ is the Shannon capacity, $V_\epsilon$ the $\epsilon$-channel
dispersion, or second-order coding rate, $Q$ the tail probability of the normal
distribution, and the constants $\underline{A}_\epsilon$ and
$\overline{A}_\epsilon$ are explicitly identified. This expression holds under
mild regularity assumptions on the channel, including nonsingularity. The gap
$\overline{A}_\epsilon - \underline{A}_\epsilon$ is one nat for weakly
symmetric channels in the Cover-Thomas sense, and typically a few nats for
other symmetric channels, for the binary symmetric channel, and for the $Z$
channel. The derivation is based on strong large-deviations analysis and
refined central limit asymptotics. A random coding scheme that achieves the
lower bound is presented. The codewords are drawn from a capacity-achieving
input distribution modified by an $O(1/\sqrt{n})$ correction term.
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1311.0195 | On the Listsize Capacity with Feedback | cs.IT math.IT | The listsize capacity of a discrete memoryless channel is the largest
transmission rate for which the expectation---or, more generally, the $\rho$-th
moment---of the number of messages that could have produced the output of the
channel approaches one as the blocklength tends to infinity. We show that for
channels with feedback this rate is upper-bounded by the maximum of Gallager's
$E_0$ function divided by $\rho$, and that equality holds when the zero-error
capacity of the channel is positive. To establish this inequality we prove that
feedback does not increase the cutoff rate. Relationships to other notions of
channel capacity are explored.
|
1311.0202 | A systematic comparison of supervised classifiers | cs.LG | Pattern recognition techniques have been employed in a myriad of industrial,
medical, commercial and academic applications. To tackle such a diversity of
data, many techniques have been devised. However, despite the long tradition of
pattern recognition research, there is no technique that yields the best
classification in all scenarios. Therefore, the consideration of as many as
possible techniques presents itself as an fundamental practice in applications
aiming at high accuracy. Typical works comparing methods either emphasize the
performance of a given algorithm in validation tests or systematically compare
various algorithms, assuming that the practical use of these methods is done by
experts. In many occasions, however, researchers have to deal with their
practical classification tasks without an in-depth knowledge about the
underlying mechanisms behind parameters. Actually, the adequate choice of
classifiers and parameters alike in such practical circumstances constitutes a
long-standing problem and is the subject of the current paper. We carried out a
study on the performance of nine well-known classifiers implemented by the Weka
framework and compared the dependence of the accuracy with their configuration
parameter configurations. The analysis of performance with default parameters
revealed that the k-nearest neighbors method exceeds by a large margin the
other methods when high dimensional datasets are considered. When other
configuration of parameters were allowed, we found that it is possible to
improve the quality of SVM in more than 20% even if parameters are set
randomly. Taken together, the investigation conducted in this paper suggests
that, apart from the SVM implementation, Weka's default configuration of
parameters provides an performance close the one achieved with the optimal
configuration.
|
1311.0222 | Online Learning with Multiple Operator-valued Kernels | cs.LG stat.ML | We consider the problem of learning a vector-valued function f in an online
learning setting. The function f is assumed to lie in a reproducing Hilbert
space of operator-valued kernels. We describe two online algorithms for
learning f while taking into account the output structure. A first contribution
is an algorithm, ONORMA, that extends the standard kernel-based online learning
algorithm NORMA from scalar-valued to operator-valued setting. We report a
cumulative error bound that holds both for classification and regression. We
then define a second algorithm, MONORMA, which addresses the limitation of
pre-defining the output structure in ONORMA by learning sequentially a linear
combination of operator-valued kernels. Our experiments show that the proposed
algorithms achieve good performance results with low computational cost.
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1311.0244 | A Message Passing Strategy for Decentralized Connectivity Maintenance in
Agent Removal | cs.SY cs.MA | In a multi-agent system, agents coordinate to achieve global tasks through
local communications. Coordination usually requires sufficient information
flow, which is usually depicted by the connectivity of the communication
network. In a networked system, removal of some agents may cause a
disconnection. In order to maintain connectivity in agent removal, one can
design a robust network topology that tolerates a finite number of agent
losses, and/or develop a control strategy that recovers connectivity. This
paper proposes a decentralized control scheme based on a sequence of
replacements, each of which occurs between an agent and one of its immediate
neighbors. The replacements always end with an agent, whose relocation does not
cause a disconnection. We show that such an agent can be reached by a local
rule utilizing only some local information available in agents' immediate
neighborhoods. As such, the proposed message passing strategy guarantees the
connectivity maintenance in arbitrary agent removal. Furthermore, we
significantly improve the optimality of the proposed scheme by incorporating
$\delta$-criticality (i.e. the criticality of an agent in its
$\delta$-neighborhood).
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1311.0251 | Capturing Variation and Uncertainty in Human Judgment | cs.IR cs.HC | The well-studied problem of statistical rank aggregation has been applied to
comparing sports teams, information retrieval, and most recently to data
generated by human judgment. Such human-generated rankings may be substantially
different from traditional statistical ranking data. In this work, we show that
a recently proposed generalized random utility model reveals distinctive
patterns in human judgment across three different domains, and provides a
succinct representation of variance in both population preferences and
imperfect perception. In contrast, we also show that classical statistical
ranking models fail to capture important features from human-generated input.
Our work motivates the use of more flexible ranking models for representing and
describing the collective preferences or decision-making of human participants.
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1311.0258 | Convexity in source separation: Models, geometry, and algorithms | cs.IT math.IT | Source separation or demixing is the process of extracting multiple
components entangled within a signal. Contemporary signal processing presents a
host of difficult source separation problems, from interference cancellation to
background subtraction, blind deconvolution, and even dictionary learning.
Despite the recent progress in each of these applications, advances in
high-throughput sensor technology place demixing algorithms under pressure to
accommodate extremely high-dimensional signals, separate an ever larger number
of sources, and cope with more sophisticated signal and mixing models. These
difficulties are exacerbated by the need for real-time action in automated
decision-making systems.
Recent advances in convex optimization provide a simple framework for
efficiently solving numerous difficult demixing problems. This article provides
an overview of the emerging field, explains the theory that governs the
underlying procedures, and surveys algorithms that solve them efficiently. We
aim to equip practitioners with a toolkit for constructing their own demixing
algorithms that work, as well as concrete intuition for why they work.
|
1311.0262 | Tracking Deformable Parts via Dynamic Conditional Random Fields | cs.CV cs.MM | Despite the success of many advanced tracking methods in this area, tracking
targets with drastic variation of appearance such as deformation, view change
and partial occlusion in video sequences is still a challenge in practical
applications. In this letter, we take these serious tracking problems into
account simultaneously, proposing a dynamic graph based model to track object
and its deformable parts at multiple resolutions. The method introduces well
learned structural object detection models into object tracking applications as
prior knowledge to deal with deformation and view change. Meanwhile, it
explicitly formulates partial occlusion by integrating spatial potentials and
temporal potentials with an unparameterized occlusion handling mechanism in the
dynamic conditional random field framework. Empirical results demonstrate that
the method outperforms state-of-the-art trackers on different challenging video
sequences.
|
1311.0274 | Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional
Regression | math.ST cs.IT cs.LG math.IT stat.ME stat.TH | We consider the problem of fitting the parameters of a high-dimensional
linear regression model. In the regime where the number of parameters $p$ is
comparable to or exceeds the sample size $n$, a successful approach uses an
$\ell_1$-penalized least squares estimator, known as Lasso. Unfortunately,
unlike for linear estimators (e.g., ordinary least squares), no
well-established method exists to compute confidence intervals or p-values on
the basis of the Lasso estimator. Very recently, a line of work
\cite{javanmard2013hypothesis, confidenceJM, GBR-hypothesis} has addressed this
problem by constructing a debiased version of the Lasso estimator. In this
paper, we study this approach for random design model, under the assumption
that a good estimator exists for the precision matrix of the design. Our
analysis improves over the state of the art in that it establishes nearly
optimal \emph{average} testing power if the sample size $n$ asymptotically
dominates $s_0 (\log p)^2$, with $s_0$ being the sparsity level (number of
non-zero coefficients). Earlier work obtains provable guarantees only for much
larger sample size, namely it requires $n$ to asymptotically dominate $(s_0
\log p)^2$.
In particular, for random designs with a sparse precision matrix we show that
an estimator thereof having the required properties can be computed
efficiently. Finally, we evaluate this approach on synthetic data and compare
it with earlier proposals.
|
1311.0314 | Guaranteed sparse signal recovery with highly coherent sensing matrices | math.NA cs.IT math.IT | Compressive sensing is a methodology for the reconstruction of sparse or
compressible signals using far fewer samples than required by the Nyquist
criterion. However, many of the results in compressive sensing concern random
sampling matrices such as Gaussian and Bernoulli matrices. In common physically
feasible signal acquisition and reconstruction scenarios such as
super-resolution of images, the sensing matrix has a non-random structure with
highly correlated columns. Here we present a compressive sensing type recovery
algorithm, called Partial Inversion (PartInv), that overcomes the correlations
among the columns. We provide theoretical justification as well as empirical
comparisons.
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1311.0320 | An Improved Solution for Restricted and Uncertain TRQ | cs.DB | CSPTRQ is an interesting problem and its has attracted much attention. The
CSPTRQ is a variant of the traditional PTRQ. As objects moving in a
constrained-space are common, clearly, it can also find many applications. At
the first sight, our problem can be easily tackled by extending existing
methods used to answer the PTRQ. Unfortunately, those classical techniques are
not well suitable for our problem, due to a set of new challenges. We develop
targeted solutions and demonstrate the efficiency and effectiveness of the
proposed methods through extensive experiments.
|
1311.0324 | An axiomatic characterization of generalized entropies under analyticity
condition | cs.IT math.IT | We present the characterization of the Nath, R\'enyi and
Havrda-Charv\'at-Tsallis entropies under the assumption that they are analytic
function with respect to the distribution dimension, unlike the the previous
characterizations, which supposes that they are expandable maximized for
uniform distribution.
|
1311.0339 | A Novel Term Weighing Scheme Towards Efficient Crawl of Textual
Databases | cs.IR | The Hidden Web is the vast repository of informational databases available
only through search form interfaces, accessible by therein typing a set of
keywords in the search forms. Typically, a Hidden Web crawler is employed to
autonomously discover and download pages from the Hidden Web. Traditional
hidden web crawlers do not provide the search engines with an optimal search
experience because of the excessive number of search requests posed through the
form interface so as to exhaustively crawl and retrieve the contents of the
target hidden web database. Here in our work, we provide a framework to
investigate the problem of optimal search and curtail it by proposing an
effective query term selection approach based on the frequency & distribution
of terms in the document database. The paper focuses on developing a
term-weighing scheme called VarDF (acronym for variable document frequency)
that can ease the identification of optimal terms to be used as queries on the
interface for maximizing the achieved coverage of the crawler which in turn
will facilitate the search engine to have a diversified and expanded index. We
experimentally evaluate the effectiveness of our approach on a manually created
database of documents in the area of Information Retrieval.
|
1311.0347 | A Survey on Routing and Data Dissemination in Opportunistic Mobile
Social Networks | cs.NI cs.SI | Opportunistic mobile social networks (MSNs) are modern paradigms of delay
tolerant networks that consist of mobile users with social characteristics. The
users in MSNs communicate with each other to share data objects. In this
setting, humans are the carriers of mobile devices, hence their social features
such as movement patterns, similarities, and interests can be exploited to
design efficient data forwarding algorithms. In this paper, an overview of
routing and data dissemination issues in the context of opportunistic MSNs is
presented, with focus on (1) MSN characteristics, (2) human mobility models,
(3) dynamic community detection methods, and (4) routing and data dissemination
protocols. Firstly, characteristics of MSNs which lead to the exposure of
patterns of interaction among mobile users are examined. Secondly, properties
of human mobility models are discussed and recently proposed mobility models
are surveyed. Thirdly, community detection and evolution analysis algorithms
are investigated. Then, a comparative review of state-of-the-art routing and
data dissemination algorithms for MSNs is presented, with special attention
paid to critical issues like context-awareness and user selfishness. Based on
the literature review, some important open issues are finally discussed.
|
1311.0350 | Sequential Mining: Patterns and Algorithms Analysis | cs.DB | This paper presents and analysis the common existing sequential pattern
mining algorithms. It presents a classifying study of sequential pattern-mining
algorithms into five extensive classes. First, on the basis of Apriori-based
algorithm, second on Breadth First Search-based strategy, third on Depth First
Search strategy, fourth on sequential closed-pattern algorithm and five on the
basis of incremental pattern mining algorithms. At the end, a comparative
analysis is done on the basis of important key features supported by various
algorithms. This study gives an enhancement in the understanding of the
approaches of sequential pattern mining.
|
1311.0351 | Rough matroids based on coverings | cs.AI | The introduction of covering-based rough sets has made a substantial
contribution to the classical rough sets. However, many vital problems in rough
sets, including attribution reduction, are NP-hard and therefore the algorithms
for solving them are usually greedy. Matroid, as a generalization of linear
independence in vector spaces, it has a variety of applications in many fields
such as algorithm design and combinatorial optimization. An excellent
introduction to the topic of rough matroids is due to Zhu and Wang. On the
basis of their work, we study the rough matroids based on coverings in this
paper. First, we investigate some properties of the definable sets with respect
to a covering. Specifically, it is interesting that the set of all definable
sets with respect to a covering, equipped with the binary relation of inclusion
$\subseteq$, constructs a lattice. Second, we propose the rough matroids based
on coverings, which are a generalization of the rough matroids based on
relations. Finally, some properties of rough matroids based on coverings are
explored. Moreover, an equivalent formulation of rough matroids based on
coverings is presented. These interesting and important results exhibit many
potential connections between rough sets and matroids.
|
1311.0352 | Why robots? A survey on the roles and benefits of social robots in the
therapy of children with autism | cs.RO cs.CY cs.HC | This paper reviews the use of socially interactive robots to assist in the
therapy of children with autism. The extent to which the robots were successful
in helping the children in their social, emotional, and communication deficits
was investigated. Child-robot interactions were scrutinized with respect to the
different target behaviors that are to be elicited from a child during therapy.
These behaviors were thoroughly examined with respect to a childs development
needs. Most importantly, experimental data from the surveyed works were
extracted and analyzed in terms of the target behaviors and how each robot was
used during a therapy session to achieve these behaviors. The study concludes
by categorizing the different therapeutic roles that these robots were observed
to play, and highlights the important design features that enable them to
achieve high levels of effectiveness in autism therapy.
|
1311.0355 | On symmetric continuum opinion dynamics | math.DS cs.MA cs.SY | This paper investigates the asymptotic behavior of some common opinion
dynamic models in a continuum of agents. We show that as long as the
interactions among the agents are symmetric, the distribution of the agents'
opinion converges. We also investigate whether convergence occurs in a stronger
sense than merely in distribution, namely, whether the opinion of almost every
agent converges. We show that while this is not the case in general, it becomes
true under plausible assumptions on inter-agent interactions, namely that
agents with similar opinions exert a non-negligible pull on each other, or that
the interactions are entirely determined by their opinions via a smooth
function.
|
1311.0388 | Non-linear Task-Space Disturbance Observer for Position Regulation of
Redundant Robot Arms against Perturbations in 3D Environments | cs.RO | Many day-to-day activities require the dexterous manipulation of a redundant
humanoid arm in complex 3D environments. However, position regulation of such
robot arm systems becomes very difficult in presence of non-linear
uncertainties in the system. Also, perturbations exist due to various unwanted
interactions with obstacles for clumsy environments in which obstacle avoidance
is not possible, and this makes position regulation even more difficult. This
report proposes a non-linear task-space disturbance observer by virtue of which
position regulation of such robotic systems can be achieved in spite of such
perturbations and uncertainties. Simulations are conducted using a 7-DOF
redundant robot arm system to show the effectiveness of the proposed method.
These results are then compared with the case of a conventional mass-damper
based task-space disturbance observer to show the enhancement in performance
using the developed concept. This proposed method is then applied to a
controller which exhibits human-like motion characteristics for reaching a
target. Arbitrary perturbations in the form of interactions with obstacles are
introduced in its path. Results show that the robot end-effector successfully
continues to move in its path of a human-like quasi-straight trajectory even if
the joint trajectories deviated by a considerable amount due to the
perturbations. These results are also compared with that of the unperturbed
motion of the robot which further prove the significance of the developed
scheme.
|
1311.0391 | Deterministic Sequences for Compressive MIMO Channel Estimation | cs.IT math.IT | This paper considers the problem of pilot design for compressive
multiple-input multiple-output (MIMO) channel estimation. In particular, we are
interested in estimating the channels for multiple transmitters simultaneously
when the pilot sequences are shorter than the combined channels. Existing works
on this topic demonstrated that tools from compressed sensing theory can yield
accurate multichannel estimation provided that each pilot sequence is randomly
generated. Here, we propose constructing the pilot sequence for each
transmitter from a small set of deterministic sequences. We derive a
theoretical lower bound on the length of the pilot sequences that guarantees
the multichannel estimation with high probability. Simulation results are
provided to demonstrate the performance of the proposed method.
|
1311.0396 | Data-based approximate policy iteration for nonlinear continuous-time
optimal control design | cs.SY math.OC stat.ML | This paper addresses the model-free nonlinear optimal problem with
generalized cost functional, and a data-based reinforcement learning technique
is developed. It is known that the nonlinear optimal control problem relies on
the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a
nonlinear partial differential equation that is generally impossible to be
solved analytically. Even worse, most of practical systems are too complicated
to establish their accurate mathematical model. To overcome these difficulties,
we propose a data-based approximate policy iteration (API) method by using real
system data rather than system model. Firstly, a model-free policy iteration
algorithm is derived for constrained optimal control problem and its
convergence is proved, which can learn the solution of HJB equation and optimal
control policy without requiring any knowledge of system mathematical model.
The implementation of the algorithm is based on the thought of actor-critic
structure, where actor and critic neural networks (NNs) are employed to
approximate the control policy and cost function, respectively. To update the
weights of actor and critic NNs, a least-square approach is developed based on
the method of weighted residuals. The whole data-based API method includes two
parts, where the first part is implemented online to collect real system
information, and the second part is conducting offline policy iteration to
learn the solution of HJB equation and the control policy. Then, the data-based
API algorithm is simplified for solving unconstrained optimal control problem
of nonlinear and linear systems. Finally, we test the efficiency of the
data-based API control design method on a simple nonlinear system, and further
apply it to a rotational/translational actuator system. The simulation results
demonstrate the effectiveness of the proposed method.
|
1311.0404 | Physical-Layer Security with Multiuser Scheduling in Cognitive Radio
Networks | cs.IT math.IT | In this paper, we consider a cognitive radio network that consists of one
cognitive base station (CBS) and multiple cognitive users (CUs) in the presence
of multiple eavesdroppers, where CUs transmit their data packets to CBS under a
primary user's quality of service (QoS) constraint while the eavesdroppers
attempt to intercept the cognitive transmissions from CUs to CBS. We
investigate the physical-layer security against eavesdropping attacks in the
cognitive radio network and propose the user scheduling scheme to achieve
multiuser diversity for improving the security level of cognitive transmissions
with a primary QoS constraint. Specifically, a cognitive user (CU) that
satisfies the primary QoS requirement and maximizes the achievable secrecy rate
of cognitive transmissions is scheduled to transmit its data packet. For the
comparison purpose, we also examine the traditional multiuser scheduling and
the artificial noise schemes. We analyze the achievable secrecy rate and
intercept probability of the traditional and proposed multiuser scheduling
schemes as well as the artificial noise scheme in Rayleigh fading environments.
Numerical results show that given a primary QoS constraint, the proposed
multiuser scheduling scheme generally outperforms the traditional multiuser
scheduling and the artificial noise schemes in terms of the achievable secrecy
rate and intercept probability. In addition, we derive the diversity order of
the proposed multiuser scheduling scheme through an asymptotic intercept
probability analysis and prove that the full diversity is obtained by using the
proposed multiuser scheduling.
|
1311.0413 | Information, Computation, Cognition. Agency-based Hierarchies of Levels | cs.AI | Nature can be seen as informational structure with computational dynamics
(info-computationalism), where an (info-computational) agent is needed for the
potential information of the world to actualize. Starting from the definition
of information as the difference in one physical system that makes a difference
in another physical system, which combines Bateson and Hewitt definitions, the
argument is advanced for natural computation as a computational model of the
dynamics of the physical world where information processing is constantly going
on, on a variety of levels of organization. This setting helps elucidating the
relationships between computation, information, agency and cognition, within
the common conceptual framework, which has special relevance for biology and
robotics.
|
1311.0423 | Phase Transitions and Cosparse Tomographic Recovery of Compound Solid
Bodies from Few Projections | math.NA cs.IT math.IT | We study unique recovery of cosparse signals from limited-angle tomographic
measurements of two- and three-dimensional domains. Admissible signals belong
to the union of subspaces defined by all cosupports of maximal cardinality
$\ell$ with respect to the discrete gradient operator. We relate $\ell$ both to
the number of measurements and to a nullspace condition with respect to the
measurement matrix, so as to achieve unique recovery by linear programming.
These results are supported by comprehensive numerical experiments that show a
high correlation of performance in practice and theoretical predictions.
Despite poor properties of the measurement matrix from the viewpoint of
compressed sensing, the class of uniquely recoverable signals basically seems
large enough to cover practical applications, like contactless quality
inspection of compound solid bodies composed of few materials.
|
1311.0433 | An Iterative Geometric Mean Decomposition Algorithm for MIMO
Communications Systems | cs.IT math.IT | This paper presents an iterative geometric mean decomposition (IGMD)
algorithm for multiple-input-multiple-output (MIMO) wireless communications. In
contrast to the existing GMD algorithms, the proposed IGMD does not require the
explicit computation of the geometric mean of positive singular values of the
channel matrix and hence is more suitable for hardware implementation. The
proposed IGMD has a regular structure and can be easily adapted to solve
problems with different dimensions. We show that the proposed IGMD is
guaranteed to converge to the perfect GMD under certain sufficient condition.
Three different constructions of the proposed algorithm are proposed and
compared through computer simulations. Numerical results show that the proposed
algorithm quickly attains comparable performance to that of the true GMD within
only a few iterations.
|
1311.0438 | Modeling Vanilla Option prices: A simulation study by an implicit method | cs.CE | Option contracts can be valued by using the Black-Scholes equation, a partial
differential equation with initial conditions. An exact solution for European
style options is known. The computation time and the error need to be minimized
simultaneously. In this paper, the authors have solved the Black-Scholes
equation by employing a reasonably accurate implicit method. Options with known
analytic solutions have been evaluated. Furthermore, an overall second order
accurate space and time discretization is proposed in this paper Keywords:
Computational finance, implicit methods, finite differences, call/put options.
|
1311.0442 | Extremal properties of tropical eigenvalues and solutions to tropical
optimization problems | math.OC cs.SY | An unconstrained optimization problem is formulated in terms of tropical
mathematics to minimize a functional that is defined on a vector set by a
matrix and calculated through multiplicative conjugate transposition. For some
particular cases, the minimum in the problem is known to be equal to the
tropical spectral radius of the matrix. We examine the problem in the common
setting of a general idempotent semifield. A complete direct solution in a
compact vector form is obtained to this problem under fairly general
conditions. The result is extended to solve new tropical optimization problems
with more general objective functions and inequality constraints. Applications
to real-world problems that arise in project scheduling are presented. To
illustrate the results obtained, numerical examples are also provided.
|
1311.0456 | In-Band Full-Duplex Wireless: Challenges and Opportunities | cs.IT math.IT | In-band full-duplex (IBFD) operation has emerged as an attractive solution
for increasing the throughput of wireless communication systems and networks.
With IBFD, a wireless terminal is allowed to transmit and receive
simultaneously in the same frequency band. This tutorial paper reviews the main
concepts of IBFD wireless. Because one the biggest practical impediments to
IBFD operation is the presence of self-interference, i.e., the interference
caused by an IBFD node's own transmissions to its desired receptions, this
tutorial surveys a wide range of IBFD self-interference mitigation techniques.
Also discussed are numerous other research challenges and opportunities in the
design and analysis of IBFD wireless systems.
|
1311.0459 | A Lossy Graph Model for Decoding Delay Reduction in Instantly Decodable
Network Coding | cs.IT math.IT | In this paper, we study the broadcast decoding delay performance of
generalized instantly decodable network coding (G-IDNC) in the lossy feedback
scenario. The problem is formulated as a maximum weight clique problem over the
G-IDNC graph in [1]. In order to further minimize the decoding delay, we
introduce in this paper the lossy G-IDNC graph (LG-IDNC). Whereas the G-IDNC
graph represents only doubtless combinable packets, the LG-IDNC graph
represents also uncertain packet combinations when the expected decoding delay
of the encoded packet is lower than the individual expected decoding delay of
each packet encoded in it. Since the maximum weight clique problem is known to
be NP-hard, we use the heuristic introduced in [2] to discover the maximum
weight clique in the LG-IDNC graph and finally we compare the decoding delay
performance of LG-IDNC and G-IDNC graphs through extensive simulations.
Numerical results show that our new LG-IDNC graph formulation outperforms the
G-IDNC graph formulation in all situations and achieves significant improvement
in the decoding delay especially when the feedback erasure probability is
higher than the packet erasure probability.
|
1311.0460 | An Adaptive Amoeba Algorithm for Shortest Path Tree Computation in
Dynamic Graphs | cs.NE | This paper presents an adaptive amoeba algorithm to address the shortest path
tree (SPT) problem in dynamic graphs. In dynamic graphs, the edge weight
updates consists of three categories: edge weight increases, edge weight
decreases, the mixture of them. Existing work on this problem solve this issue
through analyzing the nodes influenced by the edge weight updates and recompute
these affected vertices. However, when the network becomes big, the process
will become complex. The proposed method can overcome the disadvantages of the
existing approaches. The most important feature of this algorithm is its
adaptivity. When the edge weight changes, the proposed algorithm can recognize
the affected vertices and reconstruct them spontaneously. To evaluate the
proposed adaptive amoeba algorithm, we compare it with the Label Setting
algorithm and Bellman-Ford algorithm. The comparison results demonstrate the
effectiveness of the proposed method.
|
1311.0461 | An asymptotic formula in q for the number of [n,k] q-ary MDS codes | cs.IT math.AG math.IT | We obtain an asymptotic formula in q for the number of MDS codes of length n
and dimension k over a finite field with q elements.
|
1311.0466 | Thompson Sampling for Complex Bandit Problems | stat.ML cs.LG | We consider stochastic multi-armed bandit problems with complex actions over
a set of basic arms, where the decision maker plays a complex action rather
than a basic arm in each round. The reward of the complex action is some
function of the basic arms' rewards, and the feedback observed may not
necessarily be the reward per-arm. For instance, when the complex actions are
subsets of the arms, we may only observe the maximum reward over the chosen
subset. Thus, feedback across complex actions may be coupled due to the nature
of the reward function. We prove a frequentist regret bound for Thompson
sampling in a very general setting involving parameter, action and observation
spaces and a likelihood function over them. The bound holds for
discretely-supported priors over the parameter space and without additional
structural properties such as closed-form posteriors, conjugate prior structure
or independence across arms. The regret bound scales logarithmically with time
but, more importantly, with an improved constant that non-trivially captures
the coupling across complex actions due to the structure of the rewards. As
applications, we derive improved regret bounds for classes of complex bandit
problems involving selecting subsets of arms, including the first nontrivial
regret bounds for nonlinear MAX reward feedback from subsets.
|
1311.0468 | Thompson Sampling for Online Learning with Linear Experts | stat.ML cs.LG | In this note, we present a version of the Thompson sampling algorithm for the
problem of online linear generalization with full information (i.e., the
experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a
Gaussian prior and time-varying Gaussian likelihoods, and we show that it
essentially reduces to Kalai and Vempala's Follow-the-Perturbed-Leader
strategy, with exponentially distributed noise replaced by Gaussian noise. This
implies sqrt(T) regret bounds for Thompson sampling (with time-varying
likelihood) for online learning with full information.
|
1311.0505 | Automated Change Detection and Reactive Clustering in Multivariate
Streaming Data | cs.DB | Many automated systems need the capability of automatic change detection
without the given detection threshold. This paper presents an automated change
detection algorithm in streaming multivariate data. Two overlapping windows are
used to quantify the changes. While a window is used as the reference window
from which the clustering is created, the other called the current window
captures the newly incoming data points. A newly incoming data point can be
considered a change point if it is not a member of any cluster. As our
clustering-based change detector does not require detection threshold, it is an
automated detector. Based on this change detector, we propose a reactive
clustering algorithm for streaming data. Our empirical results show that, our
clustering-based change detector works well with multivariate streaming data.
The detection accuracy depends on the number of clusters in the reference
window, the window width.
|
1311.0529 | Networks of Innovation in 3D Printing | cs.HC cs.SI | Innovation inside companies is difficult to see. But an emerging online
community of inventors who publicly post 3D CAD drawings of their work provide
a way to observe - and perhaps amplify - innovation. In this paper we analyze
the network structure of Thingiverse, a website oriented toward 3D printing.
This form of printing blurs the line between creating information and
manufacturing objects: drawings can be sent to devices that build 3D objects
out of many materials, including resin, ceramics, and metal. As an exploratory
study, we analyzed the structure of Thingiverse links. Our results suggest that
analysis of remix network structure may provide ways of tracing innovation
processes and detecting the emergence of new ideas, combination of disparate
ideas.
|
1311.0534 | Accurate curve fits of IAPWS data for high-pressure, high-temperature
single-phase liquid water based on the stiffened gas equation of state | cs.CE | We present a series of optimal (in the sense of least-squares) curve fits for
the stiffened gas equation of state for single-phase liquid water. At high
pressures and (subcritical) temperatures, the parameters produced by these
curve fits are found to have very small relative errors: less than $1\%$ in the
pressure model, and less than $2\%$ in the temperature model. At low pressures
and temperatures, especially near the liquid-vapor transition line, the error
in the curve fits increases rapidly. The smallest pressure value for which
curve fits are reported in the present work is 25 MPa, high enough to ensure
that the fluid remains a single-phase liquid up to the maximum subcritical
temperature of approximately 647K.
|
1311.0536 | The SPARQL2XQuery Interoperability Framework. Utilizing Schema Mapping,
Schema Transformation and Query Translation to Integrate XML and the Semantic
Web | cs.DB | The Web of Data is an open environment consisting of a great number of large
inter-linked RDF datasets from various domains. In this environment,
organizations and companies adopt the Linked Data practices utilizing Semantic
Web (SW) technologies, in order to publish their data and offer SPARQL
endpoints (i.e., SPARQL-based search services). On the other hand, the dominant
standard for information exchange in the Web today is XML. The SW and XML
worlds and their developed infrastructures are based on different data models,
semantics and query languages. Thus, it is crucial to develop interoperability
mechanisms that allow the Web of Data users to access XML datasets, using
SPARQL, from their own working environments. It is unrealistic to expect that
all the existing legacy data (e.g., Relational, XML, etc.) will be transformed
into SW data. Therefore, publishing legacy data as Linked Data and providing
SPARQL endpoints over them has become a major research challenge. In this
direction, we introduce the SPARQL2XQuery Framework which creates an
interoperable environment, where SPARQL queries are automatically translated to
XQuery queries, in order to access XML data across the Web. The SPARQL2XQuery
Framework provides a mapping model for the expression of OWL-RDF/S to XML
Schema mappings as well as a method for SPARQL to XQuery translation. To this
end, our Framework supports both manual and automatic mapping specification
between ontologies and XML Schemas. In the automatic mapping specification
scenario, the SPARQL2XQuery exploits the XS2OWL component which transforms XML
Schemas into OWL ontologies. Finally, extensive experiments have been conducted
in order to evaluate the schema transformation, mapping generation, query
translation and query evaluation efficiency, using both real and synthetic
datasets.
|
1311.0541 | Free-configuration Biased Sampling for Motion Planning: Errata | cs.RO cs.AI | This document contains improved and updated proofs of convergence for the
sampling method presented in our paper "Free-configuration Biased Sampling for
Motion Planning".
|
1311.0546 | On the non-randomness of maximum Lempel Ziv complexity sequences of
finite size | nlin.CD cs.IT math.IT | Random sequences attain the highest entropy rate. The estimation of entropy
rate for an ergodic source can be done using the Lempel Ziv complexity measure
yet, the exact entropy rate value is only reached in the infinite limit. We
prove that typical random sequences of finite length fall short of the maximum
Lempel-Ziv complexity, contrary to common belief. We discuss that, for a finite
length, maximum Lempel-Ziv sequences can be built from a well defined
generating algorithm, which makes them of low Kolmogorov-Chaitin complexity,
quite the opposite to randomness. It will be discussed that Lempel-Ziv measure
is, in this sense, less general than Kolmogorov-Chaitin complexity, as it can
be fooled by an intelligent enough agent. The latter will be shown to be the
case for the binary expansion of certain irrational numbers. Maximum Lempel-Ziv
sequences induce a normalization that gives good estimates of entropy rate for
several sources, while keeping bounded values for all sequence length, making
it an alternative to other normalization schemes in use.
|
1311.0576 | Approximate Message Passing-based Compressed Sensing Reconstruction with
Generalized Elastic Net Prior | cs.IT math.IT | In this paper, we study the compressed sensing reconstruction problem with
generalized elastic net prior (GENP), where a sparse signal is sampled via a
noisy underdetermined linear observation system, and an additional initial
estimation of the signal (the GENP) is available during the reconstruction. We
first incorporate the GENP into the LASSO and the approximate message passing
(AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then
investigate the parameter selection, state evolution, and noise-sensitivity
analysis of GENP-AMP. We show that, thanks to the GENP, there is no phase
transition boundary in the proposed frameworks, i.e., the reconstruction error
is bounded in the entire plane. The error is also smaller than those of the
standard AMP and scalar denoising. A practical parameterless version of the
GENP-AMP is also developed, which does not need to know the sparsity of the
unknown signal and the variance of the GENP. Simulation results are presented
to verify the efficiency of the proposed schemes.
|
1311.0598 | Q-Gaussian Swarm Quantum Particle Intelligence on Predicting Global
Minimum of Potential Energy Function | cs.NE | We present a newly developed -Gaussian Swarm Quantum-like Particle
Optimization (q-GSQPO) algorithm to determine the global minimum of the
potential energy function. Swarm Quantum-like Particle Optimization (SQPO)
algorithms have been derived using different attractive potential fields to
represent swarm particles moving in a quantum environment, where the one which
uses a harmonic oscillator potential as attractive field is considered as an
improved version. In this paper, we propose a new SQPO that uses -Gaussian
probability density function for the attractive potential field (q-GSQPO)
rather than Gaussian one (GSQPO) which corresponds to harmonic potential. The
performance of the q-GSQPO is compared against the GSQPO. The new algorithm
outperforms the GSQPO on most of the time in convergence to the global optimum
by increasing the efficiency of sampling the phase space and avoiding the
premature convergence to local minima. Moreover, the computational efforts were
comparable for both algorithms. We tested the algorithm to determine the lowest
energy configurations of a particle moving in a 2, 5, 10, and 50 dimensional
spaces.
|
1311.0636 | A Parallel SGD method with Strong Convergence | cs.LG cs.DC | This paper proposes a novel parallel stochastic gradient descent (SGD) method
that is obtained by applying parallel sets of SGD iterations (each set
operating on one node using the data residing in it) for finding the direction
in each iteration of a batch descent method. The method has strong convergence
properties. Experiments on datasets with high dimensional feature spaces show
the value of this method.
|
1311.0646 | A Parallel Compressive Imaging Architecture for One-Shot Acquisition | cs.CV astro-ph.IM | A limitation of many compressive imaging architectures lies in the sequential
nature of the sensing process, which leads to long sensing times. In this paper
we present a novel architecture that uses fewer detectors than the number of
reconstructed pixels and is able to acquire the image in a single acquisition.
This paves the way for the development of video architectures that acquire
several frames per second. We specifically address the diffraction problem,
showing that deconvolution normally used to recover diffraction blur can be
replaced by convolution of the sensing matrix, and how measurements of a 0/1
physical sensing matrix can be converted to -1/1 compressive sensing matrix
without any extra acquisitions. Simulations of our architecture show that the
image quality is comparable to that of a classic Compressive Imaging camera,
whereas the proposed architecture avoids long acquisition times due to
sequential sensing. This one-shot procedure also allows to employ a fixed
sensing matrix instead of a complex device such as a Digital Micro Mirror array
or Spatial Light Modulator. It also enables imaging at bandwidths where these
are not efficient.
|
1311.0667 | Developing a Visual Interactive Search History Exploration System | cs.IR cs.HC | As users advance in their search within a system, different queries are
conducted and various results are examined by them. These objects form an
implicit individual library representing the acquired knowledge. In our
research we aim to supply the user with visualizations of the search history
and interaction methods to organize the history. The fundamental question is
what role search history exploration can play in the users search process. In
this paper we want to introduce Ideas of a prototypical system for search
history exploration and discuss methods to address the questions mentioned
above.
|
1311.0680 | Geo-located Twitter as the proxy for global mobility patterns | cs.SI physics.soc-ph | In the advent of a pervasive presence of location sharing services
researchers gained an unprecedented access to the direct records of human
activity in space and time. This paper analyses geo-located Twitter messages in
order to uncover global patterns of human mobility. Based on a dataset of
almost a billion tweets recorded in 2012 we estimate volumes of international
travelers in respect to their country of residence. We examine mobility
profiles of different nations looking at the characteristics such as mobility
rate, radius of gyration, diversity of destinations and a balance of the
inflows and outflows. The temporal patterns disclose the universal seasons of
increased international mobility and the peculiar national nature of overseen
travels. Our analysis of the community structure of the Twitter mobility
network, obtained with the iterative network partitioning, reveals spatially
cohesive regions that follow the regional division of the world. Finally, we
validate our result with the global tourism statistics and mobility models
provided by other authors, and argue that Twitter is a viable source to
understand and quantify global mobility patterns.
|
1311.0701 | On Fast Dropout and its Applicability to Recurrent Networks | stat.ML cs.LG cs.NE | Recurrent Neural Networks (RNNs) are rich models for the processing of
sequential data. Recent work on advancing the state of the art has been focused
on the optimization or modelling of RNNs, mostly motivated by adressing the
problems of the vanishing and exploding gradients. The control of overfitting
has seen considerably less attention. This paper contributes to that by
analyzing fast dropout, a recent regularization method for generalized linear
models and neural networks from a back-propagation inspired perspective. We
show that fast dropout implements a quadratic form of an adaptive,
per-parameter regularizer, which rewards large weights in the light of
underfitting, penalizes them for overconfident predictions and vanishes at
minima of an unregularized training loss. The derivatives of that regularizer
are exclusively based on the training error signal. One consequence of this is
the absense of a global weight attractor, which is particularly appealing for
RNNs, since the dynamics are not biased towards a certain regime. We positively
test the hypothesis that this improves the performance of RNNs on four musical
data sets.
|
1311.0707 | Generative Modelling for Unsupervised Score Calibration | stat.ML cs.LG | Score calibration enables automatic speaker recognizers to make
cost-effective accept / reject decisions. Traditional calibration requires
supervised data, which is an expensive resource. We propose a 2-component GMM
for unsupervised calibration and demonstrate good performance relative to a
supervised baseline on NIST SRE'10 and SRE'12. A Bayesian analysis demonstrates
that the uncertainty associated with the unsupervised calibration parameter
estimates is surprisingly small.
|
1311.0716 | Artificial Intelligence in Humans | cs.AI | In this paper, I put forward that in many instances, thinking mechanisms are
equivalent to artificial intelligence modules programmed into the human mind.
|
1311.0758 | Observation of large-scale multi-agent based simulations | cs.MA | The computational cost of large-scale multi-agent based simulations (MABS)
can be extremely important, especially if simulations have to be monitored for
validation purposes. In this paper, two methods, based on self-observation and
statistical survey theory, are introduced in order to optimize the computation
of observations in MABS. An empirical comparison of the computational cost of
these methods is performed on a toy problem.
|
1311.0776 | The Composition Theorem for Differential Privacy | cs.DS cs.CR cs.IT math.IT | Sequential querying of differentially private mechanisms degrades the overall
privacy level. In this paper, we answer the fundamental question of
characterizing the level of overall privacy degradation as a function of the
number of queries and the privacy levels maintained by each privatization
mechanism. Our solution is complete: we prove an upper bound on the overall
privacy level and construct a sequence of privatization mechanisms that
achieves this bound. The key innovation is the introduction of an operational
interpretation of differential privacy (involving hypothesis testing) and the
use of new data processing inequalities. Our result improves over the
state-of-the-art, and has immediate applications in several problems studied in
the literature including differentially private multi-party computation.
|
1311.0790 | A Discontinuous Galerkin Time Domain Framework for Periodic Structures
Subject To Oblique Excitation | cs.CE | A nodal Discontinuous Galerkin (DG) method is derived for the analysis of
time-domain (TD) scattering from doubly periodic PEC/dielectric structures
under oblique interrogation. Field transformations are employed to elaborate a
formalism that is free from any issues with causality that are common when
applying spatial periodic boundary conditions simultaneously with incident
fields at arbitrary angles of incidence. An upwind numerical flux is derived
for the transformed variables, which retains the same form as it does in the
original Maxwell problem for domains without explicitly imposed periodicity.
This, in conjunction with the amenability of the DG framework to non-conformal
meshes, provides a natural means of accurately solving the first order TD
Maxwell equations for a number of periodic systems of engineering interest.
Results are presented that substantiate the accuracy and utility of our method.
|
1311.0800 | Distributed Exploration in Multi-Armed Bandits | cs.LG | We study exploration in Multi-Armed Bandits in a setting where $k$ players
collaborate in order to identify an $\epsilon$-optimal arm. Our motivation
comes from recent employment of bandit algorithms in computationally intensive,
large-scale applications. Our results demonstrate a non-trivial tradeoff
between the number of arm pulls required by each of the players, and the amount
of communication between them. In particular, our main result shows that by
allowing the $k$ players to communicate only once, they are able to learn
$\sqrt{k}$ times faster than a single player. That is, distributing learning to
$k$ players gives rise to a factor $\sqrt{k}$ parallel speed-up. We complement
this result with a lower bound showing this is in general the best possible. On
the other extreme, we present an algorithm that achieves the ideal factor $k$
speed-up in learning performance, with communication only logarithmic in
$1/\epsilon$.
|
1311.0801 | Using Surface-Motions for Locomotion of Microscopic Robots in Viscous
Fluids | cs.RO physics.bio-ph | Microscopic robots could perform tasks with high spatial precision, such as
acting in biological tissues on the scale of individual cells, provided they
can reach precise locations. This paper evaluates the feasibility of in vivo
locomotion for micron-size robots. Two appealing methods rely only on surface
motions: steady tangential motion and small amplitude oscillations. These
methods contrast with common microorganism propulsion based on flagella or
cilia, which are more likely to damage nearby cells if used by robots made of
stiff materials. The power potentially available to robots in tissue supports
speeds ranging from one to hundreds of microns per second, over the range of
viscosities found in biological tissue. We discuss design trade-offs among
propulsion method, speed, power, shear forces and robot shape, and relate those
choices to robot task requirements. This study shows that realizing such
locomotion requires substantial improvements in fabrication capabilities and
material properties over current technology.
|
1311.0805 | On the inequality of the 3V's of Big Data Architectural Paradigms: A
case for heterogeneity | cs.DB | The well-known 3V architectural paradigm for Big Data introduced by Laney
(2011), provides a simplified framework for defining the architecture of a big
data platform to be deployed in various scenarios tackling processing of
massive datasets. While additional components such as Variability and Veracity
have been discussed as an extension to the 3V model, the basic components
(volume, variety, velocity) provide a quantitative framework while variability
and veracity target a more qualitative approach. In this paper we argue why the
basic 3V's are not equal due to the different requirements that need to be
covered in case higher demands for a particular "V". Similar to other
conjectures such as the CAP theorem 3V based architectures differ on their
implementation. We call this paradigm heterogeneity and we provide a taxonomy
of the existing tools (as of 2013) covering the Hadoop ecosystem from the
perspective of heterogeneity. This paper contributes on the understanding of
the Hadoop ecosystem from the perspective of different workloads and aims to
help researchers and practitioners on the design of scalable platforms
targeting different operational needs.
|
1311.0810 | On the emergence of an "intention field" for socially cohesive agents | physics.soc-ph cond-mat.stat-mech cs.SI | We argue that when a social convergence mechanism exists and is strong
enough, one should expect the emergence of a well defined "field", i.e. a
slowly evolving, local quantity around which individual attributes fluctuate in
a finite range. This condensation phenomenon is well illustrated by the
Deffuant-Weisbuch opinion model for which we provide a natural extension to
allow for spatial heterogeneities. We show analytically and numerically that
the resulting dynamics of the emergent field is a noisy diffusion equation that
has a slow dynamics. This random diffusion equation reproduces the long-ranged,
logarithmic decrease of the correlation of spatial voting patterns empirically
found in [1, 2]. Interestingly enough, we find that when the social cohesion
mechanism becomes too weak, cultural cohesion breaks down completely, in the
sense that the distribution of intentions/opinions becomes infinitely broad. No
emerging field exists in this case. All these analytical findings are confirmed
by numerical simulations of an agent-based model.
|
1311.0822 | Properties of maximum Lempel-Ziv complexity strings | nlin.CD cs.IT math.IT | The properties of maximum Lempel-Ziv complexity strings are studied for the
binary case. A comparison between MLZs and random strings is carried out. The
length profile of both type of sequences show different distribution functions.
The non-stationary character of the MLZs are discussed. The issue of
sensitiveness to noise is also addressed. An empirical ansatz is found that
fits well to the Lempel-Ziv complexity of the MLZs for all lengths up to $10^6$
symbols.
|
1311.0830 | The Squared-Error of Generalized LASSO: A Precise Analysis | cs.IT math.IT math.OC stat.ML | We consider the problem of estimating an unknown signal $x_0$ from noisy
linear observations $y = Ax_0 + z\in R^m$. In many practical instances, $x_0$
has a certain structure that can be captured by a structure inducing convex
function $f(\cdot)$. For example, $\ell_1$ norm can be used to encourage a
sparse solution. To estimate $x_0$ with the aid of $f(\cdot)$, we consider the
well-known LASSO method and provide sharp characterization of its performance.
We assume the entries of the measurement matrix $A$ and the noise vector $z$
have zero-mean normal distributions with variances $1$ and $\sigma^2$
respectively. For the LASSO estimator $x^*$, we attempt to calculate the
Normalized Square Error (NSE) defined as $\frac{\|x^*-x_0\|_2^2}{\sigma^2}$ as
a function of the noise level $\sigma$, the number of observations $m$ and the
structure of the signal. We show that, the structure of the signal $x_0$ and
choice of the function $f(\cdot)$ enter the error formulae through the summary
parameters $D(cone)$ and $D(\lambda)$, which are defined as the Gaussian
squared-distances to the subdifferential cone and to the $\lambda$-scaled
subdifferential, respectively. The first LASSO estimator assumes a-priori
knowledge of $f(x_0)$ and is given by $\arg\min_{x}\{{\|y-Ax\|_2}~\text{subject
to}~f(x)\leq f(x_0)\}$. We prove that its worst case NSE is achieved when
$\sigma\rightarrow 0$ and concentrates around $\frac{D(cone)}{m-D(cone)}$.
Secondly, we consider $\arg\min_{x}\{\|y-Ax\|_2+\lambda f(x)\}$, for some
$\lambda\geq 0$. This time the NSE formula depends on the choice of $\lambda$
and is given by $\frac{D(\lambda)}{m-D(\lambda)}$. We then establish a mapping
between this and the third estimator $\arg\min_{x}\{\frac{1}{2}\|y-Ax\|_2^2+
\lambda f(x)\}$. Finally, for a number of important structured signal classes,
we translate our abstract formulae to closed-form upper bounds on the NSE.
|
1311.0833 | A Comparative Study on Linguistic Feature Selection in Sentiment
Polarity Classification | cs.CL | Sentiment polarity classification is perhaps the most widely studied topic.
It classifies an opinionated document as expressing a positive or negative
opinion. In this paper, using movie review dataset, we perform a comparative
study with different single kind linguistic features and the combinations of
these features. We find that the classic topic-based classifier(Naive Bayes and
Support Vector Machine) do not perform as well on sentiment polarity
classification. And we find that with some combination of different linguistic
features, the classification accuracy can be boosted a lot. We give some
reasonable explanations about these boosting outcomes.
|
1311.0841 | A multi-terabyte relational database for geo-tagged social network data | cs.DB | Despite their relatively low sampling factor, the freely available, randomly
sampled status streams of Twitter are very useful sources of geographically
embedded social network data. To statistically analyze the information Twitter
provides via these streams, we have collected a year's worth of data and built
a multi-terabyte relational database from it. The database is designed for fast
data loading and to support a wide range of studies focusing on the statistics
and geographic features of social networks, as well as on the linguistic
analysis of tweets. In this paper we present the method of data collection, the
database design, the data loading procedure and special treatment of geo-tagged
and multi-lingual data. We also provide some SQL recipes for computing network
statistics.
|
1311.0897 | Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames | math.FA cs.IT cs.SI math.IT | We consider the problem of designing spectral graph filters for the
construction of dictionaries of atoms that can be used to efficiently represent
signals residing on weighted graphs. While the filters used in previous
spectral graph wavelet constructions are only adapted to the length of the
spectrum, the filters proposed in this paper are adapted to the distribution of
graph Laplacian eigenvalues, and therefore lead to atoms with better
discriminatory power. Our approach is to first characterize a family of systems
of uniformly translated kernels in the graph spectral domain that give rise to
tight frames of atoms generated via generalized translation on the graph. We
then warp the uniform translates with a function that approximates the
cumulative spectral density function of the graph Laplacian eigenvalues. We use
this approach to construct computationally efficient, spectrum-adapted, tight
vertex-frequency and graph wavelet frames. We give numerous examples of the
resulting spectrum-adapted graph filters, and also present an illustrative
example of vertex-frequency analysis using the proposed construction.
|
1311.0902 | FiWi Access Networks Based on Next-Generation PON and Gigabit-Class WLAN
Technologies: A Capacity and Delay Analysis (Extended Version) | cs.IT cs.NI math.IT | Current Gigabit-class passive optical networks (PONs) evolve into
next-generation PONs, whereby high-speed 10+ Gb/s time division multiplexing
(TDM) and long-reach wavelength-broadcasting/routing wavelength division
multiplexing (WDM) PONs are promising near-term candidates. On the other hand,
next-generation wireless local area networks (WLANs) based on frame aggregation
techniques will leverage physical layer enhancements, giving rise to
Gigabit-class very high throughput (VHT) WLANs. In this paper, we develop an
analytical framework for evaluating the capacity and delay performance of a
wide range of routing algorithms in converged fiber-wireless (FiWi) broadband
access networks based on different next-generation PONs and a Gigabit-class
multi-radio multi-channel WLAN-mesh front-end. Our framework is very flexible
and incorporates arbitrary frame size distributions, traffic matrices,
optical/wireless propagation delays, data rates, and fiber faults. We verify
the accuracy of our probabilistic analysis by means of simulation for the
wireless and wireless-optical-wireless operation modes of various FiWi network
architectures under peer-to-peer, upstream, uniform, and nonuniform traffic
scenarios. The results indicate that our proposed optimized FiWi routing
algorithm (OFRA) outperforms minimum (wireless) hop and delay routing in terms
of throughput for balanced and unbalanced traffic loads, at the expense of a
slightly increased mean delay at small to medium traffic loads.
|
1311.0909 | Capacity and Delay Analysis of Next-Generation Passive Optical Networks
(NG-PONs) - Extended Version | cs.IT cs.NI math.IT | Building on the Ethernet Passive Optical Network (EPON) and Gigabit PON
(GPON) standards, Next-Generation (NG) PONs (i) provide increased data rates,
split ratios, wavelengths counts, and fiber lengths, as well as (ii) allow for
all-optical integration of access and metro networks. In this paper we provide
a comprehensive probabilistic analysis of the capacity (maximum mean packet
throughput) and packet delay of subnetworks that can be used to form NG-PONs.
Our analysis can cover a wide range of NG-PONs through taking the minimum
capacity of the subnetworks making up the NG-PON and weighing the packet delays
of the subnetworks. Our numerical and simulation results indicate that our
analysis quite accurately characterizes the throughput-delay performance of
EPON/GPON tree networks, including networks upgraded with higher data rates and
wavelength counts. Our analysis also characterizes the trade-offs and
bottlenecks when integrating EPON/GPON tree networks across a metro area with a
ring, a Passive Star Coupler (PSC), or an Arrayed Waveguide Grating (AWG) for
uniform and non-uniform traffic. To the best of our knowledge, the presented
analysis is the first to consider multiple PONs interconnected via a metro
network.
|
1311.0914 | A Divide-and-Conquer Solver for Kernel Support Vector Machines | cs.LG | The kernel support vector machine (SVM) is one of the most widely used
classification methods; however, the amount of computation required becomes the
bottleneck when facing millions of samples. In this paper, we propose and
analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the
division step, we partition the kernel SVM problem into smaller subproblems by
clustering the data, so that each subproblem can be solved independently and
efficiently. We show theoretically that the support vectors identified by the
subproblem solution are likely to be support vectors of the entire kernel SVM
problem, provided that the problem is partitioned appropriately by kernel
clustering. In the conquer step, the local solutions from the subproblems are
used to initialize a global coordinate descent solver, which converges quickly
as suggested by our analysis. By extending this idea, we develop a multilevel
Divide-and-Conquer SVM algorithm with adaptive clustering and early prediction
strategy, which outperforms state-of-the-art methods in terms of training
speed, testing accuracy, and memory usage. As an example, on the covtype
dataset with half-a-million samples, DC-SVM is 7 times faster than LIBSVM in
obtaining the exact SVM solution (to within $10^{-6}$ relative error) which
achieves 96.15% prediction accuracy. Moreover, with our proposed early
prediction strategy, DC-SVM achieves about 96% accuracy in only 12 minutes,
which is more than 100 times faster than LIBSVM.
|
1311.0942 | Resource Allocation for Cost Minimization in Limited Feedback MU-MIMO
Systems with Delay Guarantee | cs.IT math.IT | In this paper, we design a resource allocation framework for the
delay-sensitive Multi-User MIMO (MU-MIMO) broadcast system with limited
feedback. Considering the scarcity and interrelation of the transmit power and
feedback bandwidth, it is imperative to optimize the two resources in a joint
and efficient manner while meeting the delay-QoS requirement. Based on the
effective bandwidth theory, we first obtain a closed-form expression of average
violation probability with respect to a given delay requirement as a function
of transmit power and codebook size of feedback channel. By minimizing the
total resource cost, we derive an optimal joint resource allocation scheme,
which can flexibly adjust the transmit power and feedback bandwidth according
to the characteristics of the system. Moreover, through asymptotic analysis,
some simple resource allocation schemes are presented. Finally, the theoretical
claims are validated by numerical results.
|
1311.0944 | Connectivity for matroids based on rough sets | cs.AI | In mathematics and computer science, connectivity is one of the basic
concepts of matroid theory: it asks for the minimum number of elements which
need to be removed to disconnect the remaining nodes from each other. It is
closely related to the theory of network flow problems. The connectivity of a
matroid is an important measure of its robustness as a network. Therefore, it
is very necessary to investigate the conditions under which a matroid is
connected. In this paper, the connectivity for matroids is studied through
relation-based rough sets. First, a symmetric and transitive relation is
introduced from a general matroid and its properties are explored from the
viewpoint of matroids. Moreover, through the relation introduced by a general
matroid, an undirected graph is generalized. Specifically, the connection of
the graph can be investigated by the relation-based rough sets. Second, we
study the connectivity for matroids by means of relation-based rough sets and
some conditions under which a general matroid is connected are presented.
Finally, it is easy to prove that the connectivity for a general matroid with
some special properties and its induced undirected graph is equivalent. These
results show an important application of relation-based rough sets to matroids.
|
1311.0950 | Off-The-Grid Spectral Compressed Sensing With Prior Information | cs.IT math.IT | Recent research in off-the-grid compressed sensing (CS) has demonstrated
that, under certain conditions, one can successfully recover a spectrally
sparse signal from a few time-domain samples even though the dictionary is
continuous. In this paper, we extend off-the-grid CS to applications where some
prior information about spectrally sparse signal is known. We specifically
consider cases where a few contributing frequencies or poles, but not their
amplitudes or phases, are known a priori. Our results show that equipping
off-the-grid CS with the known-poles algorithm can increase the probability of
recovering all the frequency components.
|
1311.0959 | Validation of a Control Algorithm for Human-like Reaching Motion using
7-DOF Arm and 19-DOF Hand-Arm Systems | cs.RO | This technical report gives an overview of our work on control algorithms
dealing with redundant robot systems for achieving human-like motion
characteristics. Previously, we developed a novel control law to exhibit
human-motion characteristics in redundant robot arm systems as well as
arm-trunk systems for reaching tasks [1], [2]. This newly developed method
nullifies the need for the computation of pseudo-inverse of Jacobian while the
formulation and optimization of any artificial performance index is not
necessary. The time-varying properties of the muscle stiffness and damping as
well as the low-pass filter characteristics of human muscles have been modeled
by the proposed control law to generate human-motion characteristics for
reaching motion like quasi-straight line trajectory of the end-effector and
symmetric bell shaped velocity profile. This report focuses on the experiments
performed using a 7-DOF redundant robot-arm system which proved the
effectiveness of this algorithm in imitating human-like motion characteristics.
In addition, we extended this algorithm to a 19-DOF Hand-Arm System for a
reach-to-grasp task. Simulations using the 19-DOF Hand-Arm System show the
effectiveness of the proposed scheme for effective human-like hand-arm
coordination in reach-to-grasp tasks for pinch and envelope grasps on objects
of different shapes such as a box, a cylinder, and a sphere.
|
1311.0966 | Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems | cs.NE q-bio.NC | Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been
demonstrated to perform efficiently in a variety of applications, such as
dimensionality reduction, feature learning, and classification. Their
implementation on neuromorphic hardware platforms emulating large-scale
networks of spiking neurons can have significant advantages from the
perspectives of scalability, power dissipation and real-time interfacing with
the environment. However the traditional RBM architecture and the commonly used
training algorithm known as Contrastive Divergence (CD) are based on discrete
updates and exact arithmetics which do not directly map onto a dynamical neural
substrate. Here, we present an event-driven variation of CD to train a RBM
constructed with Integrate & Fire (I&F) neurons, that is constrained by the
limitations of existing and near future neuromorphic hardware platforms. Our
strategy is based on neural sampling, which allows us to synthesize a spiking
neural network that samples from a target Boltzmann distribution. The recurrent
activity of the network replaces the discrete steps of the CD algorithm, while
Spike Time Dependent Plasticity (STDP) carries out the weight updates in an
online, asynchronous fashion. We demonstrate our approach by training an RBM
composed of leaky I&F neurons with STDP synapses to learn a generative model of
the MNIST hand-written digit dataset, and by testing it in recognition,
generation and cue integration tasks. Our results contribute to a machine
learning-driven approach for synthesizing networks of spiking neurons capable
of carrying out practical, high-level functionality.
|
1311.0989 | Large Margin Distribution Machine | cs.LG | Support vector machine (SVM) has been one of the most popular learning
algorithms, with the central idea of maximizing the minimum margin, i.e., the
smallest distance from the instances to the classification boundary. Recent
theoretical results, however, disclosed that maximizing the minimum margin does
not necessarily lead to better generalization performances, and instead, the
margin distribution has been proven to be more crucial. In this paper, we
propose the Large margin Distribution Machine (LDM), which tries to achieve a
better generalization performance by optimizing the margin distribution. We
characterize the margin distribution by the first- and second-order statistics,
i.e., the margin mean and variance. The LDM is a general learning approach
which can be used in any place where SVM can be applied, and its superiority is
verified both theoretically and empirically in this paper.
|
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