id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1306.5362 | A Statistical Perspective on Algorithmic Leveraging | stat.ME cs.LG stat.ML | One popular method for dealing with large-scale data sets is sampling. For
example, by using the empirical statistical leverage scores as an importance
sampling distribution, the method of algorithmic leveraging samples and
rescales rows/columns of data matrices to reduce the data size before
performing computations on the subproblem. This method has been successful in
improving computational efficiency of algorithms for matrix problems such as
least-squares approximation, least absolute deviations approximation, and
low-rank matrix approximation. Existing work has focused on algorithmic issues
such as worst-case running times and numerical issues associated with providing
high-quality implementations, but none of it addresses statistical aspects of
this method.
In this paper, we provide a simple yet effective framework to evaluate the
statistical properties of algorithmic leveraging in the context of estimating
parameters in a linear regression model with a fixed number of predictors. We
show that from the statistical perspective of bias and variance, neither
leverage-based sampling nor uniform sampling dominates the other. This result
is particularly striking, given the well-known result that, from the
algorithmic perspective of worst-case analysis, leverage-based sampling
provides uniformly superior worst-case algorithmic results, when compared with
uniform sampling. Based on these theoretical results, we propose and analyze
two new leveraging algorithms. A detailed empirical evaluation of existing
leverage-based methods as well as these two new methods is carried out on both
synthetic and real data sets. The empirical results indicate that our theory is
a good predictor of practical performance of existing and new leverage-based
algorithms and that the new algorithms achieve improved performance.
|
1306.5365 | On Investigating EMD Parameters to Search for Gravitational Waves | gr-qc cs.CE math.NA | The Hilbert-Huang transform (HHT) is a novel, adaptive approach to time
series analysis. It does not impose a basis set on the data or otherwise make
assumptions about the data form, and so the time--frequency decomposition is
not limited by spreading due to uncertainty. Because of the high resolution of
the time--frequency, we investigate the possibility of the application of the
HHT to the search for gravitational waves. It is necessary to determine some
parameters in the empirical mode decomposition (EMD), which is a component of
the HHT, and in this paper we propose and demonstrate a method to determine the
optimal values of the parameters to use in the search for gravitational waves.
|
1306.5377 | Thresholds of Random Quasi-Abelian Codes | cs.IT math.IT | For a random quasi-abelian code of rate $r$, it is shown that the GV-bound is
a threshold point: if $r$ is less than the GV-bound at $\delta$, then the
probability of the relative distance of the random code being greater than
$\delta$ is almost 1; whereas, if $r$ is bigger than the GV-bound at $\delta$,
then the probability is almost 0. As a consequence, there exist many
asymptotically good quasi-abelian codes with any parameters attaining the
GV-bound.
|
1306.5383 | Patterns in the occupational mobility network of the higher education
graduates. Comparative study in 12 EU countries | physics.soc-ph cs.SI | The article investigates the properties of the occupational mobility network
(OMN) in 12 EU countries. Using REFLEX database we construct for each country
an empirical OMN that reflects the job movements of the university graduates,
during the first five years after graduation (1999 - 2005). The nodes are
represented by the occupations coded at 3 digits according to ISCO-88 and the
links are weighted with the number of graduates switching from one occupation
to another. We construct the networks as weighted and directed. This
comparative study allows us to see what are the common patterns in the OMN over
different EU labor markets.
|
1306.5390 | P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for
Image Denoising | cs.DC cs.CV | This paper presents a parallel Salt and Pepper (SP) noise removal algorithm
in a grey level digital image based on the Hypergraph Based Root Mean Square
(HGRMS) approach. HGRMS is generic algorithm for identifying noisy pixels in
any digital image using a two level hierarchical serial approach. However, for
SP noise removal, we reduce this algorithm to a parallel model by introducing a
cardinality matrix and an iteration factor, k, which helps us reduce the
dependencies in the existing approach. We also observe that the performance of
the serial implementation is better on smaller images, but once the threshold
is achieved in terms of image resolution, its computational complexity
increases drastically. We test P-HGRMS using standard images from the Berkeley
Segmentation dataset on NVIDIAs Compute Unified Device Architecture (CUDA) for
noise identification and attenuation. We also compare the noise removal
efficiency of the proposed algorithm using Peak Signal to Noise Ratio (PSNR) to
the existing approach. P-HGRMS maintains the noise removal efficiency and
outperforms its sequential counterpart by 6 to 18 times (6x - 18x) in
computational efficiency.
|
1306.5412 | Electronically Tunable Voltage-Mode Biquad Filter/Oscillator Based On
CCCCTAs | cs.SY | In this paper, a circuit employing current controlled current conveyor
trans-conductance amplifiers (CCCCTAs) as active element is proposed which can
function both as biquad filter and oscillator. It uses two CCCCTAs and two
capacitors. As a biquad filter it can realizes all the standard filtering
functions (low pass, band pass, high pass, band reject and all pass) in
voltage-mode and provides the feature of electronically and orthogonal control
of pole frequency and quality factor through biasing current(s) of CCCCTAs. The
proposed circuit can also be worked as oscillator without changing the circuit
topology. Without any resistors and using capacitors, the proposed circuit is
suitable for IC fabrication. The validity of proposed filter is verified
through PSPICE simulations.
|
1306.5424 | The Fine Classification of Conjunctive Queries and Parameterized
Logarithmic Space Complexity | cs.CC cs.DB cs.LO | We perform a fundamental investigation of the complexity of conjunctive query
evaluation from the perspective of parameterized complexity. We classify sets
of boolean conjunctive queries according to the complexity of this problem.
Previous work showed that a set of conjunctive queries is fixed-parameter
tractable precisely when the set is equivalent to a set of queries having
bounded treewidth. We present a fine classification of query sets up to
parameterized logarithmic space reduction. We show that, in the bounded
treewidth regime, there are three complexity degrees and that the properties
that determine the degree of a query set are bounded pathwidth and bounded tree
depth. We also engage in a study of the two higher degrees via logarithmic
space machine characterizations and complete problems. Our work yields a
significantly richer perspective on the complexity of conjunctive queries and,
at the same time, suggests new avenues of research in parameterized complexity.
|
1306.5441 | Supervisor Localization of Discrete-Event Systems based on State Tree
Structures | cs.SY | Recently we developed supervisor localization, a top-down approach to
distributed control of discrete-event systems in the Ramadge-Wonham supervisory
control framework. Its essence is the decomposition of monolithic (global)
control action into local control strategies for the individual agents. In this
paper, we establish a counterpart supervisor localization theory in the
framework of State Tree Structures, known to be efficient for control design of
very large systems. In the new framework, we introduce the new concepts of
local state tracker, local control function, and state-based local-global
control equivalence. As before, we prove that the collective localized control
behavior is identical to the monolithic optimal (i.e. maximally permissive) and
nonblocking controlled behavior. In addition, we propose a new and more
efficient localization algorithm which exploits BDD computation. Finally we
demonstrate our localization approach on a model for a complex semiconductor
manufacturing system.
|
1306.5473 | The Geospatial Characteristics of a Social Movement Communication
Network | cs.CY cs.SI physics.data-an physics.soc-ph | Social movements rely in large measure on networked communication
technologies to organize and disseminate information relating to the movements'
objectives. In this work we seek to understand how the goals and needs of a
protest movement are reflected in the geographic patterns of its communication
network, and how these patterns differ from those of stable political
communication. To this end, we examine an online communication network
reconstructed from over 600,000 tweets from a thirty-six week period covering
the birth and maturation of the American anticapitalist movement, Occupy Wall
Street. We find that, compared to a network of stable domestic political
communication, the Occupy Wall Street network exhibits higher levels of
locality and a hub and spoke structure, in which the majority of non-local
attention is allocated to high-profile locations such as New York, California,
and Washington D.C. Moreover, we observe that information flows across state
boundaries are more likely to contain framing language and references to the
media, while communication among individuals in the same state is more likely
to reference protest action and specific places and and times. Tying these
results to social movement theory, we propose that these features reflect the
movement's efforts to mobilize resources at the local level and to develop
narrative frames that reinforce collective purpose at the national level.
|
1306.5474 | The Digital Evolution of Occupy Wall Street | cs.CY cs.SI physics.data-an physics.soc-ph | We examine the temporal evolution of digital communication activity relating
to the American anti-capitalist movement Occupy Wall Street. Using a
high-volume sample from the microblogging site Twitter, we investigate changes
in Occupy participant engagement, interests, and social connectivity over a
fifteen month period starting three months prior to the movement's first
protest action. The results of this analysis indicate that, on Twitter, the
Occupy movement tended to elicit participation from a set of highly
interconnected users with pre-existing interests in domestic politics and
foreign social movements. These users, while highly vocal in the months
immediately following the birth of the movement, appear to have lost interest
in Occupy related communication over the remainder of the study period.
|
1306.5480 | Characterizing Ambiguity in Light Source Invariant Shape from Shading | cs.CV q-bio.NC | Shape from shading is a classical inverse problem in computer vision. This
shape reconstruction problem is inherently ill-defined; it depends on the
assumed light source direction. We introduce a novel mathematical formulation
for calculating local surface shape based on covariant derivatives of the
shading flow field, rather than the customary integral minimization or P.D.E
approaches. On smooth surfaces, we show second derivatives of brightness are
independent of the light sources and can be directly related to surface
properties. We use these measurements to define the matching local family of
surfaces that can result from any given shading patch, changing the emphasis to
characterizing ambiguity in the problem. We give an example of how these local
surface ambiguities collapse along certain image contours and how this can be
used for the reconstruction problem.
|
1306.5487 | Model Reframing by Feature Context Change | cs.LG | The feature space (including both input and output variables) characterises a
data mining problem. In predictive (supervised) problems, the quality and
availability of features determines the predictability of the dependent
variable, and the performance of data mining models in terms of
misclassification or regression error. Good features, however, are usually
difficult to obtain. It is usual that many instances come with missing values,
either because the actual value for a given attribute was not available or
because it was too expensive. This is usually interpreted as a utility or
cost-sensitive learning dilemma, in this case between misclassification (or
regression error) costs and attribute tests costs. Both misclassification cost
(MC) and test cost (TC) can be integrated into a single measure, known as joint
cost (JC). We introduce methods and plots (such as the so-called JROC plots)
that can work with any of-the-shelf predictive technique, including ensembles,
such that we re-frame the model to use the appropriate subset of attributes
(the feature configuration) during deployment time. In other words, models are
trained with the available attributes (once and for all) and then deployed by
setting missing values on the attributes that are deemed ineffective for
reducing the joint cost. As the number of feature configuration combinations
grows exponentially with the number of features we introduce quadratic methods
that are able to approximate the optimal configuration and model choices, as
shown by the experimental results.
|
1306.5511 | Binary decision making with very heterogeneous influence | physics.soc-ph cond-mat.stat-mech cs.SI | We consider an extension of a binary decision model in which nodes make
decisions based on influence-biased averages of their neighbors' states,
similar to Ising spin glasses with on-site random fields. In the limit where
these influences become very heavy-tailed, the behavior of the model
dramatically changes. On complete graphs, or graphs where nodes with large
influence have large degree, this model is characterized by a new "phase" with
an unpredictable number of macroscopic shocks, with no associated critical
phenomena. On random graphs where the degree of the most influential nodes is
small compared to population size, a predictable glassy phase without phase
transitions emerges. Analytic results about both of these new phases are
obtainable in limiting cases. We use numerical simulations to explore the model
for more general scenarios. The phases associated with very influential
decision makers are easily distinguishable experimentally from a homogeneous
influence phase in many circumstances, in the context of our simple model.
|
1306.5513 | Power Minimization in Multi-pair Two-Way Relaying | cs.IT math.IT | This doc provides some proofs in our submitted journal paper.
|
1306.5532 | Deep Learning by Scattering | cs.LG stat.ML | We introduce general scattering transforms as mathematical models of deep
neural networks with l2 pooling. Scattering networks iteratively apply complex
valued unitary operators, and the pooling is performed by a complex modulus. An
expected scattering defines a contractive representation of a high-dimensional
probability distribution, which preserves its mean-square norm. We show that
unsupervised learning can be casted as an optimization of the space contraction
to preserve the volume occupied by unlabeled examples, at each layer of the
network. Supervised learning and classification are performed with an averaged
scattering, which provides scattering estimations for multiple classes.
|
1306.5533 | Evolving Gene Regulatory Networks with Mobile DNA Mechanisms | cs.CE nlin.AO q-bio.MN | This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.
|
1306.5538 | Influence of Reciprocal links in Social Networks | physics.soc-ph cs.SI | In this Letter, we empirically study the influence of reciprocal links, in
order to understand its role in affecting the structure and function of
directed social networks. Experimental results on two representative datesets,
Sina Weibo and Douban, demonstrate that the reciprocal links indeed play a more
important role than non-reciprocal ones in both spreading information and
maintaining the network robustness. In particular, the information spreading
process can be significantly enhanced by considering the reciprocal effect. In
addition, reciprocal links are largely responsible for the connectivity and
efficiency of directed networks. This work may shed some light on the in-depth
understanding and application of the reciprocal effect in directed online
social networks.
|
1306.5550 | Spectral redemption: clustering sparse networks | cs.SI cond-mat.stat-mech physics.soc-ph stat.ML | Spectral algorithms are classic approaches to clustering and community
detection in networks. However, for sparse networks the standard versions of
these algorithms are suboptimal, in some cases completely failing to detect
communities even when other algorithms such as belief propagation can do so.
Here we introduce a new class of spectral algorithms based on a
non-backtracking walk on the directed edges of the graph. The spectrum of this
operator is much better-behaved than that of the adjacency matrix or other
commonly used matrices, maintaining a strong separation between the bulk
eigenvalues and the eigenvalues relevant to community structure even in the
sparse case. We show that our algorithm is optimal for graphs generated by the
stochastic block model, detecting communities all the way down to the
theoretical limit. We also show the spectrum of the non-backtracking operator
for some real-world networks, illustrating its advantages over traditional
spectral clustering.
|
1306.5554 | Correlated random features for fast semi-supervised learning | stat.ML cs.LG | This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised
algorithm for regression and classification. The algorithm draws on two main
ideas. First, it generates two views consisting of computationally inexpensive
random features. Second, XNV applies multiview regression using Canonical
Correlation Analysis (CCA) on unlabeled data to bias the regression towards
useful features. It has been shown that, if the views contains accurate
estimators, CCA regression can substantially reduce variance with a minimal
increase in bias. Random views are justified by recent theoretical and
empirical work showing that regression with random features closely
approximates kernel regression, implying that random views can be expected to
contain accurate estimators. We show that XNV consistently outperforms a
state-of-the-art algorithm for semi-supervised learning: substantially
improving predictive performance and reducing the variability of performance on
a wide variety of real-world datasets, whilst also reducing runtime by orders
of magnitude.
|
1306.5586 | Creating a Relational Distributed Object Store | cs.DB cs.DC | In and of itself, data storage has apparent business utility. But when we can
convert data to information, the utility of stored data increases dramatically.
It is the layering of relation atop the data mass that is the engine for such
conversion. Frank relation amongst discrete objects sporadically ingested is
rare, making the process of synthesizing such relation all the more
challenging, but the challenge must be met if we are ever to see an equivalent
business value for unstructured data as we already have with structured data.
This paper describes a novel construct, referred to as a relational distributed
object store (RDOS), that seeks to solve the twin problems of how to
persistently and reliably store petabytes of unstructured data while
simultaneously creating and persisting relations amongst billions of objects.
|
1306.5596 | An Algorithm for Constructing a Smallest Register with Non-Linear Update
Generating a Given Binary Sequence | cs.IT math.IT | Registers with Non-Linear Update (RNLUs) are a generalization of Non-Linear
Feedback Shift Registers (NLFSRs) in which both, feedback and feedforward,
connections are allowed and no chain connection between the stages is required.
In this paper, a new algorithm for constructing RNLUs generating a given binary
sequence is presented. Expected size of RNLUs constructed by the presented
algorithm is proved to be O(n/log(n/p)), where n is the sequence length and p
is the degree of parallelization. This is asymptotically smaller than the
expected size of RNLUs constructed by previous algorithms and the expected size
of LFSRs and NLFSRs generating the same sequence. The presented algorithm can
potentially be useful for many applications, including testing, wireless
communications, and cryptography.
|
1306.5601 | A Decomposition of the Max-min Fair Curriculum-based Course Timetabling
Problem | cs.AI | We propose a decomposition of the max-min fair curriculum-based course
timetabling (MMF-CB-CTT) problem. The decomposition models the room assignment
subproblem as a generalized lexicographic bottleneck optimization problem
(LBOP). We show that the generalized LBOP can be solved efficiently if the
corresponding sum optimization problem can be solved efficiently. As a
consequence, the room assignment subproblem of the MMF-CB-CTT problem can be
solved efficiently. We use this insight to improve a previously proposed
heuristic algorithm for the MMF-CB-CTT problem. Our experimental results
indicate that using the new decomposition improves the performance of the
algorithm on most of the 21 ITC2007 test instances with respect to the quality
of the best solution found. Furthermore, we introduce a measure of the quality
of a solution to a max-min fair optimization problem. This measure helps to
overcome some limitations imposed by the qualitative nature of max-min fairness
and aids the statistical evaluation of the performance of randomized algorithms
for such problems. We use this measure to show that using the new decomposition
the algorithm outperforms the original one on most instances with respect to
the average solution quality.
|
1306.5606 | Proteus: A Hierarchical Portfolio of Solvers and Transformations | cs.AI | In recent years, portfolio approaches to solving SAT problems and CSPs have
become increasingly common. There are also a number of different encodings for
representing CSPs as SAT instances. In this paper, we leverage advances in both
SAT and CSP solving to present a novel hierarchical portfolio-based approach to
CSP solving, which we call Proteus, that does not rely purely on CSP solvers.
Instead, it may decide that it is best to encode a CSP problem instance into
SAT, selecting an appropriate encoding and a corresponding SAT solver. Our
experimental evaluation used an instance of Proteus that involved four CSP
solvers, three SAT encodings, and six SAT solvers, evaluated on the most
challenging problem instances from the CSP solver competitions, involving
global and intensional constraints. We show that significant performance
improvements can be achieved by Proteus obtained by exploiting alternative
view-points and solvers for combinatorial problem-solving.
|
1306.5609 | Partial Spreads in Random Network Coding | cs.IT math.IT | Following the approach by R. K\"otter and F. R. Kschischang, we study network
codes as families of k-dimensional linear subspaces of a vector space F_q^n, q
being a prime power and F_q the finite field with q elements. In particular,
following an idea in finite projective geometry, we introduce a class of
network codes which we call "partial spread codes". Partial spread codes
naturally generalize spread codes. In this paper we provide an easy description
of such codes in terms of matrices, discuss their maximality, and provide an
efficient decoding algorithm.
|
1306.5667 | Using Genetic Programming to Model Software | cs.NE cs.AI | We study a generic program to investigate the scope for automatically
customising it for a vital current task, which was not considered when it was
first written. In detail, we show genetic programming (GP) can evolve models of
aspects of BLAST's output when it is used to map Solexa Next-Gen DNA sequences
to the human genome.
|
1306.5690 | Modifying the Entity relationship modelling notation: towards high
quality relational databases from better notated ER models | cs.DB | The entity relationship modelling using the original ER notation has been
applauded providing a natural view of data in conceptual modelling of
information systems. However, the current ER to relational model transformation
algorithm is known to be insufficient in providing a complete and accurate
representation of the ER model undertaken for transformation. In an effort to
derive better transformations from ER models, we have understood that
modifications should be introduced to both of the existing transformation
algorithm as well as to the ER notation. Introducing some new concepts, we have
adapted the original ER notation and developed a new transformation algorithm
based on the existing one. This paper presents the modified ER notation with an
ER diagram drawn based on the new notation.
|
1306.5702 | Modeling The Stable Operating Envelope For Partially Stable Combustion
Engines Using Class Imbalance Learning | cs.NE | Advanced combustion technologies such as homogeneous charge compression
ignition (HCCI) engines have a narrow stable operating region defined by
complex control strategies such as exhaust gas recirculation (EGR) and variable
valve timing among others. For such systems, it is important to identify the
operating envelope or the boundary of stable operation for diagnostics and
control purposes. Obtaining a good model of the operating envelope using
physics becomes intractable owing to engine transient effects. In this paper, a
machine learning based approach is employed to identify the stable operating
boundary of HCCI combustion directly from experimental data. Owing to imbalance
in class proportions in the data, two approaches are considered. A re-sampling
(under-sampling, over-sampling) based approach is used to develop models using
existing algorithms while a cost-sensitive approach is used to modify the
learning algorithm without modifying the data set. Support vector machines and
recently developed extreme learning machines are used for model development and
results compared against linear classification methods show that cost-sensitive
versions of ELM and SVM algorithms are well suited to model the HCCI operating
envelope. The prediction results indicate that the models have the potential to
be used for predicting HCCI instability based on sensor measurement history.
|
1306.5707 | Synthesizing Manipulation Sequences for Under-Specified Tasks using
Unrolled Markov Random Fields | cs.RO cs.AI cs.LG | Many tasks in human environments require performing a sequence of navigation
and manipulation steps involving objects. In unstructured human environments,
the location and configuration of the objects involved often change in
unpredictable ways. This requires a high-level planning strategy that is robust
and flexible in an uncertain environment. We propose a novel dynamic planning
strategy, which can be trained from a set of example sequences. High level
tasks are expressed as a sequence of primitive actions or controllers (with
appropriate parameters). Our score function, based on Markov Random Field
(MRF), captures the relations between environment, controllers, and their
arguments. By expressing the environment using sets of attributes, the approach
generalizes well to unseen scenarios. We train the parameters of our MRF using
a maximum margin learning method. We provide a detailed empirical validation of
our overall framework demonstrating successful plan strategies for a variety of
tasks.
|
1306.5720 | On the Resilience of Bipartite Networks | cs.DS cs.SI | Motivated by problems modeling the spread of infections in networks, in this
paper we explore which bipartite graphs are most resilient to widespread
infections under various parameter settings. Namely, we study bipartite
networks with a requirement of a minimum degree $d$ on one side under an
independent infection, independent transmission model. We completely
characterize the optimal graphs in the case $d=1$, which already produces
non-trivial behavior, and we give extremal results for the more general cases.
We show that in the case $d=2$, surprisingly, the optimally resilient set of
graphs includes a graph that is not one of the two "extremes" found in the case
$d=1$.
Then, we briefly examine the case where we force a connectivity requirement
instead of a one-sided degree requirement and again, we find that the set of
the most resilient graphs contains more than the two "extremes." We also show
that determining the subgraph of an arbitrary bipartite graph most resilient to
infection is NP-hard for any one-sided minimal degree $d \ge 1$.
|
1306.5776 | Two-Part Reconstruction in Compressed Sensing | cs.IT math.IT | Two-part reconstruction is a framework for signal recovery in compressed
sensing (CS), in which the advantages of two different algorithms are combined.
Our framework allows to accelerate the reconstruction procedure without
compromising the reconstruction quality. To illustrate the efficacy of our
two-part approach, we extend the author's previous Sudocodes algorithm and make
it robust to measurement noise. In a 1-bit CS setting, promising numerical
results indicate that our algorithm offers both a reduction in run-time and
improvement in reconstruction quality.
|
1306.5781 | Comparison of the Achievable Rates in OFDM and Single Carrier Modulation
with I.I.D. Inputs | cs.IT math.IT | We compare the maximum achievable rates in single-carrier and OFDM modulation
schemes, under the practical assumptions of i.i.d. finite alphabet inputs and
linear ISI with additive Gaussian noise. We show that the Shamai-Laroia
approximation serves as a bridge between the two rates: while it is well known
that this approximation is often a lower bound on the single-carrier achievable
rate, it is revealed to also essentially upper bound the OFDM achievable rate.
We apply Information-Estimation relations in order to rigorously establish this
result for both general input distributions and to sharpen it for commonly used
PAM and QAM constellations. To this end, novel bounds on MMSE estimation of PAM
inputs to a scalar Gaussian channel are derived, which may be of general
interest. Our results show that, under reasonable assumptions, optimal
single-carrier schemes may offer spectral efficiency significantly superior to
that of OFDM, motivating further research of such systems.
|
1306.5787 | Spread Spectrum Codes for Continuous-Phase Modulated Systems | cs.IT math.IT | We study the theoretical performance of a combined approach to demodulation
and decoding of binary continuous-phase modulated signals under repetition-like
codes. This technique is motivated by a need to transmit packetized or framed
data bursts in high noise regimes where many powerful, short-length codes are
ineffective. In channels with strong noise, we mathematically study the
asymptotic bit error rates of this combined approach and quantify the
performance improvement over performing demodulation and decoding separately as
the code rate increases. In this context, we also discuss a simple variant of
repetition coding involving pseudorandom code words, based on direct-sequence
spread spectrum methods, that preserves the spectral density of the encoded
signal in order to maintain resistance to narrowband interference. We describe
numerical simulations that demonstrate the advantages of this approach as an
inner code which can be used underneath modern coding schemes in high noise
environments.
|
1306.5793 | A State-Space Approach for Optimal Traffic Monitoring via Network Flow
Sampling | cs.SY cs.NI stat.AP stat.ML | The robustness and integrity of IP networks require efficient tools for
traffic monitoring and analysis, which scale well with traffic volume and
network size. We address the problem of optimal large-scale flow monitoring of
computer networks under resource constraints. We propose a stochastic
optimization framework where traffic measurements are done by exploiting the
spatial (across network links) and temporal relationship of traffic flows.
Specifically, given the network topology, the state-space characterization of
network flows and sampling constraints at each monitoring station, we seek an
optimal packet sampling strategy that yields the best traffic volume estimation
for all flows of the network. The optimal sampling design is the result of a
concave minimization problem; then, Kalman filtering is employed to yield a
sequence of traffic estimates for each network flow. We evaluate our algorithm
using real-world Internet2 data.
|
1306.5794 | Algorithm independent bounds on community detection problems and
associated transitions in stochastic block model graphs | cond-mat.stat-mech cs.SI physics.soc-ph | We derive rigorous bounds for well-defined community structure in complex
networks for a stochastic block model (SBM) benchmark. In particular, we
analyze the effect of inter-community "noise" (inter-community edges) on any
"community detection" algorithm's ability to correctly group nodes assigned to
a planted partition, a problem which has been proven to be NP complete in a
standard rendition. Our result does not rely on the use of any one particular
algorithm nor on the analysis of the limitations of inference. Rather, we turn
the problem on its head and work backwards to examine when, in the first place,
well defined structure may exist in SBMs.The method that we introduce here
could potentially be applied to other computational problems. The objective of
community detection algorithms is to partition a given network into optimally
disjoint subgraphs (or communities). Similar to k-SAT and other combinatorial
optimization problems, "community detection" exhibits different phases.
Networks that lie in the "unsolvable phase" lack well-defined structure and
thus have no partition that is meaningful. Solvable systems splinter into two
disparate phases: those in the "hard" phase and those in the "easy" phase. As
befits its name, within the easy phase, a partition is easy to achieve by known
algorithms. When a network lies in the hard phase, it still has an underlying
structure yet finding a meaningful partition which can be checked in polynomial
time requires an exhaustive computational effort that rapidly increases with
the size of the graph. When taken together, (i) the rigorous results that we
report here on when graphs have an underlying structure and (ii) recent results
concerning the limits of rather general algorithms, suggest bounds on the hard
phase.
|
1306.5809 | Weight distributions of cyclic codes with respect to pairwise coprime
order elements | cs.IT math.IT | Let $\Bbb F_r$ be an extension of a finite field $\Bbb F_q$ with $r=q^m$. Let
each $g_i$ be of order $n_i$ in $\Bbb F_r^*$ and $\gcd(n_i, n_j)=1$ for $1\leq
i \neq j \leq u$.
We define a cyclic code over $\Bbb F_q$ by
$$\mathcal C_{(q, m, n_1,n_2, ..., n_u)}=\{c(a_1, a_2, ..., a_u) : a_1, a_2,
..., a_u \in \Bbb F_r\},$$ where
$$c(a_1, a_2, ..., a_u)=({Tr}_{r/q}(\sum_{i=1}^ua_ig_i^0), ...,
{Tr}_{r/q}(\sum_{i=1}^ua_ig_i^{n-1}))$$ and $n=n_1n_2... n_u$. In this paper,
we present a method to compute the weights of $\mathcal C_{(q, m, n_1,n_2, ...,
n_u)}$. Further, we determine the weight distributions of the cyclic codes
$\mathcal C_{(q, m, n_1,n_2)}$ and $\mathcal C_{(q, m, n_1,n_2,1)}$.
|
1306.5825 | Fourier PCA and Robust Tensor Decomposition | cs.LG cs.DS stat.ML | Fourier PCA is Principal Component Analysis of a matrix obtained from higher
order derivatives of the logarithm of the Fourier transform of a
distribution.We make this method algorithmic by developing a tensor
decomposition method for a pair of tensors sharing the same vectors in rank-$1$
decompositions. Our main application is the first provably polynomial-time
algorithm for underdetermined ICA, i.e., learning an $n \times m$ matrix $A$
from observations $y=Ax$ where $x$ is drawn from an unknown product
distribution with arbitrary non-Gaussian components. The number of component
distributions $m$ can be arbitrarily higher than the dimension $n$ and the
columns of $A$ only need to satisfy a natural and efficiently verifiable
nondegeneracy condition. As a second application, we give an alternative
algorithm for learning mixtures of spherical Gaussians with linearly
independent means. These results also hold in the presence of Gaussian noise.
|
1306.5836 | Robust Decentralized Stabilization of Markovian Jump Large-Scale
Systems: A Neighboring Mode Dependent Control Approach | cs.SY | This paper is concerned with the decentralized stabilization problem for a
class of uncertain large-scale systems with Markovian jump parameters. The
controllers use local subsystem states and neighboring mode information to
generate local control inputs. A sufficient condition involving rank
constrained linear matrix inequalities is proposed for the design of such
controllers. A numerical example is given to illustrate the developed theory.
|
1306.5850 | Practical Secrecy: Bridging the Gap between Cryptography and Physical
Layer Security | cs.IT math.IT | Current security techniques can be implemented either by requiring a secret
key exchange or depending on assumptions about the communication channels. In
this paper, we show that, by using a physical layer technique known as
artificial noise, it is feasible to protect secret data without any form of
secret key exchange and any restriction on the communication channels.
Specifically, we analyze how the artificial noise can achieve practical
secrecy. By treating the artificial noise as an unshared one-time pad secret
key, we show that the proposed scheme also achieves Shannon's perfect secrecy.
Moreover, we show that achieving perfect secrecy is much easier than ensuring
non-zero secrecy capacity, especially when the eavesdropper has more antennas
than the transmitter. Focusing on the practical applications, we show that
practical secrecy and strong secrecy can be guaranteed even if the eavesdropper
attempts to remove the artificial noise. We finally show the connections
between traditional cryptography and physical layer security.
|
1306.5858 | Distributed Heuristic Forward Search for Multi-Agent Systems | cs.AI cs.DC | This paper describes a number of distributed forward search algorithms for
solving multi-agent planning problems. We introduce a distributed formulation
of non-optimal forward search, as well as an optimal version, MAD-A*. Our
algorithms exploit the structure of multi-agent problems to not only distribute
the work efficiently among different agents, but also to remove symmetries and
reduce the overall workload. The algorithms ensure that private information is
not shared among agents, yet computation is still efficient -- outperforming
current state-of-the-art distributed planners, and in some cases even
centralized search -- despite the fact that each agent has access only to
partial information.
|
1306.5883 | Line Spectrum Estimation with Probabilistic Priors | math.ST cs.IT math.IT stat.TH | For line spectrum estimation, we derive the maximum a posteriori probability
estimator where prior knowledge of frequencies is modeled probabilistically.
Since the spectrum is periodic, an appropriate distribution is the circular von
Mises distribution that can parameterize the entire range of prior certainty of
the frequencies. An efficient alternating projections method is used to solve
the resulting optimization problem. The estimator is evaluated numerically and
compared with other estimators and the Cram\'er-Rao bound.
|
1306.5884 | Design of an Agent for Answering Back in Smart Phones | cs.AI cs.HC cs.LG | The objective of the paper is to design an agent which provides efficient
response to the caller when a call goes unanswered in smartphones. The agent
provides responses through text messages, email etc stating the most likely
reason as to why the callee is unable to answer a call. Responses are composed
taking into consideration the importance of the present call and the situation
the callee is in at the moment like driving, sleeping, at work etc. The agent
makes decisons in the compostion of response messages based on the patterns it
has come across in the learning environment. Initially the user helps the agent
to compose response messages. The agent associates this message to the percept
it recieves with respect to the environment the callee is in. The user may
thereafter either choose to make to response system automatic or choose to
recieve suggestions from the agent for responses messages and confirm what is
to be sent to the caller.
|
1306.5898 | A Grammatical Inference Approach to Language-Based Anomaly Detection in
XML | cs.CR cs.DB | False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.
|
1306.5918 | A Randomized Nonmonotone Block Proximal Gradient Method for a Class of
Structured Nonlinear Programming | math.OC cs.LG cs.NA math.NA stat.ML | We propose a randomized nonmonotone block proximal gradient (RNBPG) method
for minimizing the sum of a smooth (possibly nonconvex) function and a
block-separable (possibly nonconvex nonsmooth) function. At each iteration,
this method randomly picks a block according to any prescribed probability
distribution and solves typically several associated proximal subproblems that
usually have a closed-form solution, until a certain progress on objective
value is achieved. In contrast to the usual randomized block coordinate descent
method [23,20], our method has a nonmonotone flavor and uses variable stepsizes
that can partially utilize the local curvature information of the smooth
component of objective function. We show that any accumulation point of the
solution sequence of the method is a stationary point of the problem {\it
almost surely} and the method is capable of finding an approximate stationary
point with high probability. We also establish a sublinear rate of convergence
for the method in terms of the minimal expected squared norm of certain
proximal gradients over the iterations. When the problem under consideration is
convex, we show that the expected objective values generated by RNBPG converge
to the optimal value of the problem. Under some assumptions, we further
establish a sublinear and linear rate of convergence on the expected objective
values generated by a monotone version of RNBPG. Finally, we conduct some
preliminary experiments to test the performance of RNBPG on the
$\ell_1$-regularized least-squares problem and a dual SVM problem in machine
learning. The computational results demonstrate that our method substantially
outperforms the randomized block coordinate {\it descent} method with fixed or
variable stepsizes.
|
1306.5920 | Sandwiched R\'enyi Divergence Satisfies Data Processing Inequality | quant-ph cs.IT math-ph math.IT math.MP | Sandwiched (quantum) $\alpha$-R\'enyi divergence has been recently defined in
the independent works of Wilde et al. (arXiv:1306.1586) and M\"uller-Lennert et
al (arXiv:1306.3142v1). This new quantum divergence has already found
applications in quantum information theory. Here we further investigate
properties of this new quantum divergence. In particular we show that
sandwiched $\alpha$-R\'enyi divergence satisfies the data processing inequality
for all values of $\alpha> 1$. Moreover we prove that $\alpha$-Holevo
information, a variant of Holevo information defined in terms of sandwiched
$\alpha$-R\'enyi divergence, is super-additive. Our results are based on
H\"older's inequality, the Riesz-Thorin theorem and ideas from the theory of
complex interpolation. We also employ Sion's minimax theorem.
|
1306.5960 | Computation of Diet Composition for Patients Suffering from Kidney and
Urinary Tract Diseases with the Fuzzy Genetic System | cs.AI | Determination of dietary food consumed a day for patients with diseases in
general, greatly affect the health of the body and the healing process, is no
exception for people with kidney disease and urinary tract. This paper presents
the determination of diet composition in the form of food subtance for people
with kidney and urinary tract diseases with a genetic fuzzy approach. This
approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy
logic fuzzy tools and techniques to model the components of the genetic
algorithm and adapting genetic algorithm control parameters, with the aim of
improving system performance. The Mamdani fuzzy inference model and fuzzy rules
based on population parameters and generation are used to determine the
probability of crossover and mutation, and was using In this study, 400 food
survey data along with their substances was used as test material. From the
data, a varying amount of population is established. Each chromosome has 10
genes in which the value of each gene indicates the index number of foodstuffs
in the database. The fuzzy genetic approach produces 10 best food substance and
their compositions. The composition of these foods has nutritional value in
accordance with the number of calories needed by people with kidney and urinary
tract diseases by type of food.
|
1306.5961 | Gender homophily from spatial behavior in a primary school: a
sociometric study | physics.soc-ph cs.SI | We investigate gender homophily in the spatial proximity of children (6 to 12
years old) in a French primary school, using time-resolved data on face-to-face
proximity recorded by means of wearable sensors. For strong ties, i.e., for
pairs of children who interact more than a defined threshold, we find
statistical evidence of gender preference that increases with grade. For weak
ties, conversely, gender homophily is negatively correlated with grade for
girls, and positively correlated with grade for boys. This different evolution
with grade of weak and strong ties exposes a contrasted picture of gender
homophily.
|
1306.5972 | Communication Steps for Parallel Query Processing | cs.DB | We consider the problem of computing a relational query $q$ on a large input
database of size $n$, using a large number $p$ of servers. The computation is
performed in rounds, and each server can receive only $O(n/p^{1-\varepsilon})$
bits of data, where $\varepsilon \in [0,1]$ is a parameter that controls
replication. We examine how many global communication steps are needed to
compute $q$. We establish both lower and upper bounds, in two settings. For a
single round of communication, we give lower bounds in the strongest possible
model, where arbitrary bits may be exchanged; we show that any algorithm
requires $\varepsilon \geq 1-1/\tau^*$, where $\tau^*$ is the fractional vertex
cover of the hypergraph of $q$. We also give an algorithm that matches the
lower bound for a specific class of databases. For multiple rounds of
communication, we present lower bounds in a model where routing decisions for a
tuple are tuple-based. We show that for the class of tree-like queries there
exists a tradeoff between the number of rounds and the space exponent
$\varepsilon$. The lower bounds for multiple rounds are the first of their
kind. Our results also imply that transitive closure cannot be computed in O(1)
rounds of communication.
|
1306.5982 | Activity Modeling in Smart Home using High Utility Pattern Mining over
Data Streams | cs.AI cs.DB | Smart home technology is a better choice for the people to care about
security, comfort and power saving as well. It is required to develop
technologies that recognize the Activities of Daily Living (ADLs) of the
residents at home and detect the abnormal behavior in the individual's
patterns. Data mining techniques such as Frequent pattern mining (FPM), High
Utility Pattern (HUP) Mining were used to find those activity patterns from the
collected sensor data. But applying the above technique for Activity
Recognition from the temporal sensor data stream is highly complex and
challenging task. So, a new approach is proposed for activity recognition from
sensor data stream which is achieved by constructing Frequent Pattern Stream
tree (FPS - tree). FPS is a sliding window based approach to discover the
recent activity patterns over time from data streams. The proposed work aims at
identifying the frequent pattern of the user from the sensor data streams which
are later modeled for activity recognition. The proposed FPM algorithm uses a
data structure called Linked Sensor Data Stream (LSDS) for storing the sensor
data stream information which increases the efficiency of frequent pattern
mining algorithm through both space and time. The experimental results show the
efficiency of the proposed algorithm and this FPM is further extended for
applying for power efficiency using HUP to detect the high usage of power
consumption of residents at smart home.
|
1306.5998 | DNA Reservoir Computing: A Novel Molecular Computing Approach | cs.NE cs.ET nlin.AO nlin.CD physics.bio-ph | We propose a novel molecular computing approach based on reservoir computing.
In reservoir computing, a dynamical core, called a reservoir, is perturbed with
an external input signal while a readout layer maps the reservoir dynamics to a
target output. Computation takes place as a transformation from the input space
to a high-dimensional spatiotemporal feature space created by the transient
dynamics of the reservoir. The readout layer then combines these features to
produce the target output. We show that coupled deoxyribozyme oscillators can
act as the reservoir. We show that despite using only three coupled
oscillators, a molecular reservoir computer could achieve 90% accuracy on a
benchmark temporal problem.
|
1306.6041 | Learning, Generalization, and Functional Entropy in Random Automata
Networks | cs.NE cond-mat.dis-nn nlin.AO nlin.CD physics.bio-ph | It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that
feedforward Boolean networks can learn to perform specific simple tasks and
generalize well if only a subset of the learning examples is provided for
learning. Here, we extend this body of work and show experimentally that random
Boolean networks (RBNs), where both the interconnections and the Boolean
transfer functions are chosen at random initially, can be evolved by using a
state-topology evolution to solve simple tasks. We measure the learning and
generalization performance, investigate the influence of the average node
connectivity $K$, the system size $N$, and introduce a new measure that allows
to better describe the network's learning and generalization behavior. We show
that the connectivity of the maximum entropy networks scales as a power-law of
the system size $N$. Our results show that networks with higher average
connectivity $K$ (supercritical) achieve higher memorization and partial
generalization. However, near critical connectivity, the networks show a higher
perfect generalization on the even-odd task.
|
1306.6042 | OptShrink: An algorithm for improved low-rank signal matrix denoising by
optimal, data-driven singular value shrinkage | math.ST cs.IT math.IT stat.ML stat.TH | The truncated singular value decomposition (SVD) of the measurement matrix is
the optimal solution to the_representation_ problem of how to best approximate
a noisy measurement matrix using a low-rank matrix. Here, we consider the
(unobservable)_denoising_ problem of how to best approximate a low-rank signal
matrix buried in noise by optimal (re)weighting of the singular vectors of the
measurement matrix. We exploit recent results from random matrix theory to
exactly characterize the large matrix limit of the optimal weighting
coefficients and show that they can be computed directly from data for a large
class of noise models that includes the i.i.d. Gaussian noise case.
Our analysis brings into sharp focus the shrinkage-and-thresholding form of
the optimal weights, the non-convex nature of the associated shrinkage function
(on the singular values) and explains why matrix regularization via singular
value thresholding with convex penalty functions (such as the nuclear norm)
will always be suboptimal. We validate our theoretical predictions with
numerical simulations, develop an implementable algorithm (OptShrink) that
realizes the predicted performance gains and show how our methods can be used
to improve estimation in the setting where the measured matrix has missing
entries.
|
1306.6056 | Rate-Compatible Protograph-based LDPC Codes for Inter-Symbol
Interference Channels | cs.IT math.IT | This letter produces a family of rate-compatible protograph-based LDPC codes
approaching the independent and uniformly distributed (i.u.d.) capacity of
inter-symbol interference (ISI) channels. This problem is highly nontrivial due
to the joint design of structured (protograph-based) LDPC codes and the state
structure of ISI channels. We describe a method to design nested high-rate
protograph codes by adding variable nodes to the protograph of a lower rate
code. We then design a family of rate-compatible protograph codes using the
extension method. The resulting protograph codes have iterative decoding
thresholds close to the i.u.d. capacity. Our results are supported by numerical
simulations.
|
1306.6058 | A maximal-information color to gray conversion method for document
images: Toward an optimal grayscale representation for document image
binarization | cs.CV | A novel method to convert color/multi-spectral images to gray-level images is
introduced to increase the performance of document binarization methods. The
method uses the distribution of the pixel data of the input document image in a
color space to find a transformation, called the dual transform, which balances
the amount of information on all color channels. Furthermore, in order to
reduce the intensity variations on the gray output, a color reduction
preprocessing step is applied. Then, a channel is selected as the gray value
representation of the document image based on the homogeneity criterion on the
text regions. In this way, the proposed method can provide a
luminance-independent contrast enhancement. The performance of the method is
evaluated against various images from two databases, the ICDAR'03 Robust
Reading, the KAIST and the DIBCO'09 datasets, subjectively and objectively with
promising results. The ground truth images for the images from the ICDAR'03
Robust Reading dataset have been created manually by the authors.
|
1306.6078 | A Computational Approach to Politeness with Application to Social
Factors | cs.CL cs.SI physics.soc-ph | We propose a computational framework for identifying linguistic aspects of
politeness. Our starting point is a new corpus of requests annotated for
politeness, which we use to evaluate aspects of politeness theory and to
uncover new interactions between politeness markers and context. These findings
guide our construction of a classifier with domain-independent lexical and
syntactic features operationalizing key components of politeness theory, such
as indirection, deference, impersonalization and modality. Our classifier
achieves close to human performance and is effective across domains. We use our
framework to study the relationship between politeness and social power,
showing that polite Wikipedia editors are more likely to achieve high status
through elections, but, once elevated, they become less polite. We see a
similar negative correlation between politeness and power on Stack Exchange,
where users at the top of the reputation scale are less polite than those at
the bottom. Finally, we apply our classifier to a preliminary analysis of
politeness variation by gender and community.
|
1306.6111 | Understanding the Predictive Power of Computational Mechanics and Echo
State Networks in Social Media | cs.SI cs.LG physics.soc-ph stat.AP stat.ML | There is a large amount of interest in understanding users of social media in
order to predict their behavior in this space. Despite this interest, user
predictability in social media is not well-understood. To examine this
question, we consider a network of fifteen thousand users on Twitter over a
seven week period. We apply two contrasting modeling paradigms: computational
mechanics and echo state networks. Both methods attempt to model the behavior
of users on the basis of their past behavior. We demonstrate that the behavior
of users on Twitter can be well-modeled as processes with self-feedback. We
find that the two modeling approaches perform very similarly for most users,
but that they differ in performance on a small subset of the users. By
exploring the properties of these performance-differentiated users, we
highlight the challenges faced in applying predictive models to dynamic social
data.
|
1306.6116 | Distributed Estimation and Detection with Bounded Transmissions over
Gaussian Multiple Access Channels | cs.DC cs.IT math.IT | A distributed inference scheme which uses bounded transmission functions over
a Gaussian multiple access channel is considered. When the sensor measurements
are decreasingly reliable as a function of the sensor index, the conditions on
the transmission functions under which consistent estimation and reliable
detection are possible is characterized. For the distributed estimation
problem, an estimation scheme that uses bounded transmission functions is
proved to be strongly consistent provided that the variance of the noise
samples are bounded and that the transmission function is one-to-one. The
proposed estimation scheme is compared with the amplify-and-forward technique
and its robustness to impulsive sensing noise distributions is highlighted. In
contrast to amplify-and-forward schemes, it is also shown that bounded
transmissions suffer from inconsistent estimates if the sensing noise variance
goes to infinity. For the distributed detection problem, similar results are
obtained by studying the deflection coefficient. Simulations corroborate our
analytical results.
|
1306.6122 | Downlink Rate Distribution in Heterogeneous Cellular Networks under
Generalized Cell Selection | cs.IT cs.NI math.IT | Considering both small-scale fading and long-term shadowing, we characterize
the downlink rate distribution at a typical user equipment (UE) in a
heterogeneous cellular network (HetNet), where shadowing, following any general
distribution, impacts cell selection while fading does not. Prior work either
ignores the impact of channel randomness on cell selection or lumps all the
sources of randomness into a single variable, with cell selection based on the
instantaneous signal strength, which is unrealistic. As an application of the
results, we study the impact of shadowing on load balancing in terms of the
optimal per-tier selection bias needed for rate maximization.
|
1306.6125 | Design and Implementation of an Unmanned Vehicle using a GSM Network
with Microcontrollers | cs.SY | Now-a-days, a lot of research is being carried out in the development of USVs
(Unmanned surface vehicles), UAVs (Unmanned Aerial Vehicles) etc. Now in case
of USVs generally, we have seen that wireless controlled vehicles use RF
circuits which suffer from many drawbacks such as limited working range,
limited frequency range and limited control. Moreover shooting infrared
outdoors on a bright sunny day is often problematic, since sunlight can
interfere with the infrared signal. Use of a GSM network (in the form of a
mobile phone, a cordless phone) for robotic control can overcome these
limitations. It provides the advantages of robust control, working range as
large as the coverage area of the service provider in comparison with that of
an IR system, no interference with other controllers. This paper presents a
Global System for Mobile Telecommunication (GSM) network based system which can
be used to remotely send streams of 4 bit data for control of USVs.
Furthermore, this paper describes the usage of the Dual Tone Multi-Frequency
(DTMF) function of the phone, and builds a microcontroller based circuit to
control the vehicle to demonstrate wireless data communication. Practical
result obtained showed an appreciable degree of accuracy of the system and
friendliness through the use of a microcontroller.
|
1306.6130 | Competency Tracking for English as a Second or Foreign Language Learners | cs.CL | My system utilizes the outcomes feature found in Moodle and other learning
content management systems (LCMSs) to keep track of where students are in terms
of what language competencies they have mastered and the competencies they need
to get where they want to go. These competencies are based on the Common
European Framework for (English) Language Learning. This data can be available
for everyone involved with a given student's progress (e.g. educators, parents,
supervisors and the students themselves). A given student's record of past
accomplishments can also be meshed with those of his classmates. Not only are a
student's competencies easily seen and tracked, educators can view competencies
of a group of students that were achieved prior to enrollment in the class.
This should make curriculum decision making easier and more efficient for
educators.
|
1306.6141 | One-bit Decentralized Detection with a Rao Test for Multisensor Fusion | cs.IT math.IT | In this letter we propose the Rao test as a simpler alternative to the
generalized likelihood ratio test (GLRT) for multisensor fusion. We consider
sensors observing an unknown deterministic parameter with symmetric and
unimodal noise. A decision fusion center (DFC) receives quantized sensor
observations through error-prone binary symmetric channels and makes a global
decision. We analyze the optimal quantizer thresholds and we study the
performance of the Rao test in comparison to the GLRT. Also, a theoretical
comparison is made and asymptotic performance is derived in a scenario with
homogeneous sensors. All the results are confirmed through simulations.
|
1306.6169 | Throughput and Energy Efficiency Analysis of Small Cell Networks with
Multi-antenna Base Stations | cs.IT math.IT | Small cell networks have recently been proposed as an important evolution
path for the next-generation cellular networks. However, with more and more
irregularly deployed base stations (BSs), it is becoming increasingly difficult
to quantify the achievable network throughput or energy efficiency. In this
paper, we develop an analytical framework for downlink performance evaluation
of small cell networks, based on a random spatial network model, where BSs and
users are modeled as two independent spatial Poisson point processes. A new
simple expression of the outage probability is derived, which is analytically
tractable and is especially useful with multi-antenna transmissions. This new
result is then applied to evaluate the network throughput and energy
efficiency. It is analytically shown that deploying more BSs or more BS
antennas can always increase the network throughput, but the performance gain
critically depends on the BS-user density ratio and the number of BS antennas.
On the other hand, increasing the BS density or the number of transmit antennas
will first increase and then decrease the energy efficiency if different
components of BS power consumption satisfy certain conditions, and the optimal
BS density and the optimal number of BS antennas can be found. Otherwise, the
energy efficiency will always decrease. Simulation results shall demonstrate
that our conclusions based on the random network model are general and also
hold in a regular grid-based model.
|
1306.6189 | Scaling Up Robust MDPs by Reinforcement Learning | cs.LG stat.ML | We consider large-scale Markov decision processes (MDPs) with parameter
uncertainty, under the robust MDP paradigm. Previous studies showed that robust
MDPs, based on a minimax approach to handle uncertainty, can be solved using
dynamic programming for small to medium sized problems. However, due to the
"curse of dimensionality", MDPs that model real-life problems are typically
prohibitively large for such approaches. In this work we employ a reinforcement
learning approach to tackle this planning problem: we develop a robust
approximate dynamic programming method based on a projected fixed point
equation to approximately solve large scale robust MDPs. We show that the
proposed method provably succeeds under certain technical conditions, and
demonstrate its effectiveness through simulation of an option pricing problem.
To the best of our knowledge, this is the first attempt to scale up the robust
MDPs paradigm.
|
1306.6194 | A PSO Approach for Optimum Design of Multivariable PID Controller for
nonlinear systems | cs.SY | The aim of this research is to design a PID Controller using particle swarm
optimization (PSO) algorithm for multiple-input multiple output (MIMO)
Takagi-Sugeno fuzzy model. The conventional gain tuning of PID controller (such
as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore
modern heuristics approach such as PSO are employed to enhance the capability
of traditional techniques. However, due to the computational efficiency, only
PSO will be used in this paper. The results show the advantage of the PID
tuning using PSO-based optimization approach.
|
1306.6198 | Emergent Behavior in Multipartite Large Networks: Multi-virus Epidemics | cs.SI physics.soc-ph | Epidemics in large complete networks is well established. In contrast, we
consider epidemics in non-complete networks. We establish the fluid limit
macroscopic dynamics of a multi-virus spread over a multipartite network as the
number of nodes at each partite or island grows large. The virus spread follows
a peer-to-peer random rule of infection in line with the Harris contact
process. The model conforms to an SIS (susceptible-infected-susceptible) type,
where a node is either infected or it is healthy and prone to be infected. The
local (at node level) random infection model induces the emergence of
structured dynamics at the macroscale. Namely, we prove that, as the
multipartite network grows large, the normalized Markov jump vector process
$\left(\bar{\mathbf{Y}}^\mathbf{N}(t)\right) =
\left(\bar{Y}_1^\mathbf{N}(t),\ldots, \bar{Y}_M^\mathbf{N}(t)\right)$
collecting the fraction of infected nodes at each island $i=1,\ldots,M$,
converges weakly (with respect to the Skorokhod topology on the space of
\emph{c\`{a}dl\`{a}g} sample paths) to the solution of an $M$-dimensional
vector nonlinear coupled ordinary differential equation. In the case of
multi-virus diffusion with $K\in\mathbb{N}$ distinct strains of virus, the
Markov jurmp matrix process $\left(\bar{\mathbf{Y}}^\mathbf{N}(t)\right)$,
stacking the fraction of nodes infected with virus type $j$, $j=1,\ldots,K$, at
each island $i=1,\ldots,M$, converges weakly as well to the solution of a
$\left(K\times M\right)$-dimensional vector differential equation that is also
characterized.
|
1306.6203 | A Derivation of the Asymptotic Random-Coding Prefactor | cs.IT math.IT | This paper studies the subexponential prefactor to the random-coding bound
for a given rate. Using a refinement of Gallager's bounding techniques, an
alternative proof of a recent result by Altu\u{g} and Wagner is given, and the
result is extended to the setting of mismatched decoding.
|
1306.6206 | Investigating Immune System Aging: System Dynamics and Agent-Based
Modeling | cs.CE q-bio.QM | System dynamics and agent based simulation models can both be used to model
and understand interactions of entities within a population. Our modeling work
presented here is concerned with understanding the suitability of the different
types of simulation for the immune system aging problems and comparing their
results. We are trying to answer questions such as: How fit is the immune
system given a certain age? Would an immune boost be of therapeutic value, e.g.
to improve the effectiveness of a simultaneous vaccination? Understanding the
processes of immune system aging and degradation may also help in development
of therapies that reverse some of the damages caused thus improving life
expectancy. Therefore as a first step our research focuses on T cells; major
contributors to immune system functionality. One of the main factors
influencing immune system aging is the output rate of naive T cells. Of further
interest is the number and phenotypical variety of these cells in an
individual, which will be the case study focused on in this paper.
|
1306.6239 | Near-Optimal Adaptive Compressed Sensing | cs.IT math.IT stat.ML | This paper proposes a simple adaptive sensing and group testing algorithm for
sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and
Search (CASS), is shown to be near-optimal in that it succeeds at the lowest
possible signal-to-noise-ratio (SNR) levels, improving on previous work in
adaptive compressed sensing. Like traditional compressed sensing based on
random non-adaptive design matrices, the CASS algorithm requires only k log n
measurements to recover a k-sparse signal of dimension n. However, CASS
succeeds at SNR levels that are a factor log n less than required by standard
compressed sensing. From the point of view of constructing and implementing the
sensing operation as well as computing the reconstruction, the proposed
algorithm is substantially less computationally intensive than standard
compressed sensing. CASS is also demonstrated to perform considerably better in
practice through simulation. To the best of our knowledge, this is the first
demonstration of an adaptive compressed sensing algorithm with near-optimal
theoretical guarantees and excellent practical performance. This paper also
shows that methods like compressed sensing, group testing, and pooling have an
advantage beyond simply reducing the number of measurements or tests --
adaptive versions of such methods can also improve detection and estimation
performance when compared to non-adaptive direct (uncompressed) sensing.
|
1306.6259 | Highlighting Entanglement of Cultures via Ranking of Multilingual
Wikipedia Articles | cs.SI cs.IR physics.soc-ph | How different cultures evaluate a person? Is an important person in one
culture is also important in the other culture? We address these questions via
ranking of multilingual Wikipedia articles. With three ranking algorithms based
on network structure of Wikipedia, we assign ranking to all articles in 9
multilingual editions of Wikipedia and investigate general ranking structure of
PageRank, CheiRank and 2DRank. In particular, we focus on articles related to
persons, identify top 30 persons for each rank among different editions and
analyze distinctions of their distributions over activity fields such as
politics, art, science, religion, sport for each edition. We find that local
heroes are dominant but also global heroes exist and create an effective
network representing entanglement of cultures. The Google matrix analysis of
network of cultures shows signs of the Zipf law distribution. This approach
allows to examine diversity and shared characteristics of knowledge
organization between cultures. The developed computational, data driven
approach highlights cultural interconnections in a new perspective.
|
1306.6260 | Information-Theoretic Security for the Masses | cs.CR cs.CY cs.IT math.IT | We combine interactive zero-knowledge protocols and weak physical layer
randomness properties to construct a protocol which allows bootstrapping an
IT-secure and PF-secure channel from a memorizable shared secret. The protocol
also tolerates failures of its components, still preserving most of its
security properties, which makes it accessible to regular users.
|
1306.6263 | Persian Heritage Image Binarization Competition (PHIBC 2012) | cs.CV | The first competition on the binarization of historical Persian documents and
manuscripts (PHIBC 2012) has been organized in conjunction with the first
Iranian conference on pattern recognition and image analysis (PRIA 2013). The
main objective of PHIBC 2012 is to evaluate performance of the binarization
methodologies, when applied on the Persian heritage images. This paper provides
a report on the methodology and performance of the three submitted algorithms
based on evaluation measures has been used.
|
1306.6264 | Codes on Graphs: Fundamentals | cs.IT math.IT | This paper develops a fundamental theory of realizations of linear and group
codes on general graphs using elementary group theory, including basic group
duality theory. Principal new and extended results include: normal realization
duality; analysis of systems-theoretic properties of fragments of realizations
and their connections; "minimal = trim and proper" theorem for cycle-free
codes; results showing that all constraint codes except interface nodes may be
assumed to be trim and proper, and that the interesting part of a cyclic
realization is its "2-core;" notions of observability and controllability for
fragments, and related tests; relations between state-trimness and
controllability, and dual state-trimness and observability.
|
1306.6265 | Towards Secure Two-Party Computation from the Wire-Tap Channel | cs.CR cs.IT math.IT | We introduce a new protocol for secure two-party computation of linear
functions in the semi-honest model, based on coding techniques. We first
establish a parallel between the second version of the wire-tap channel model
and secure two-party computation. This leads us to our protocol, that combines
linear coset coding and oblivious transfer techniques. Our construction
requires the use of binary intersecting codes or $q$-ary minimal codes, which
are also studied in this paper.
|
1306.6269 | Active Contour Models for Manifold Valued Image Segmentation | cs.CV | Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize an objective function designed such that the desired
object boundary is the optimal solution. Recently, imaging modalities that
produce Manifold valued images have come up, for example, DT-MRI images, vector
fields. The traditional active contour model does not work on such images. In
this paper, we generalize the active contour model to work on Manifold valued
images. As expected, our algorithm detects regions with similar Manifold values
in the image. Our algorithm also produces expected results on usual gray-scale
images, since these are nothing but trivial examples of Manifold valued images.
As another application of our general active contour model, we perform texture
segmentation on gray-scale images by first creating an appropriate Manifold
valued image. We demonstrate segmentation results for manifold valued images
and texture images.
|
1306.6281 | Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow
Reconstruction | cs.IT cs.CV math.IT stat.AP | This paper describes a coded aperture and keyed exposure approach to
compressive video measurement which admits a small physical platform, high
photon efficiency, high temporal resolution, and fast reconstruction
algorithms. The proposed projections satisfy the Restricted Isometry Property
(RIP), and hence compressed sensing theory provides theoretical guarantees on
the video reconstruction quality. Moreover, the projections can be easily
implemented using existing optical elements such as spatial light modulators
(SLMs). We extend these coded mask designs to novel dual-scale masks (DSMs)
which enable the recovery of a coarse-resolution estimate of the scene with
negligible computational cost. We develop fast numerical algorithms which
utilize both temporal correlations and optical flow in the video sequence as
well as the innovative structure of the projections. Our numerical experiments
demonstrate the efficacy of the proposed approach on short-wave infrared data.
|
1306.6288 | Information Spectrum Approach to the Source Channel Separation Theorem | cs.IT math.IT | A source-channel separation theorem for a general channel has recently been
shown by Aggrawal et. al. This theorem states that if there exist a coding
scheme that achieves a maximum distortion level d_{max} over a general channel
W, then reliable communication can be accomplished over this channel at rates
less then R(d_{max}), where R(.) is the rate distortion function of the source.
The source, however, is essentially constrained to be discrete and memoryless
(DMS). In this work we prove a stronger claim where the source is general,
satisfying only a "sphere packing optimality" feature, and the channel is
completely general. Furthermore, we show that if the channel satisfies the
strong converse property as define by Han & verdu, then the same statement can
be made with d_{avg}, the average distortion level, replacing d_{max}. Unlike
the proofs there, we use information spectrum methods to prove the statements
and the results can be quite easily extended to other situations.
|
1306.6294 | Learning Trajectory Preferences for Manipulators via Iterative
Improvement | cs.RO cs.AI cs.HC | We consider the problem of learning good trajectories for manipulation tasks.
This is challenging because the criterion defining a good trajectory varies
with users, tasks and environments. In this paper, we propose a co-active
online learning framework for teaching robots the preferences of its users for
object manipulation tasks. The key novelty of our approach lies in the type of
feedback expected from the user: the human user does not need to demonstrate
optimal trajectories as training data, but merely needs to iteratively provide
trajectories that slightly improve over the trajectory currently proposed by
the system. We argue that this co-active preference feedback can be more easily
elicited from the user than demonstrations of optimal trajectories, which are
often challenging and non-intuitive to provide on high degrees of freedom
manipulators. Nevertheless, theoretical regret bounds of our algorithm match
the asymptotic rates of optimal trajectory algorithms. We demonstrate the
generalizability of our algorithm on a variety of grocery checkout tasks, for
whom, the preferences were not only influenced by the object being manipulated
but also by the surrounding environment.\footnote{For more details and a
demonstration video, visit: \url{http://pr.cs.cornell.edu/coactive}}
|
1306.6295 | Tight Lower Bound for Linear Sketches of Moments | cs.DS cs.IT math.IT math.ST stat.TH | The problem of estimating frequency moments of a data stream has attracted a
lot of attention since the onset of streaming algorithms [AMS99]. While the
space complexity for approximately computing the $p^{\rm th}$ moment, for
$p\in(0,2]$ has been settled [KNW10], for $p>2$ the exact complexity remains
open. For $p>2$ the current best algorithm uses $O(n^{1-2/p}\log n)$ words of
space [AKO11,BO10], whereas the lower bound is of $\Omega(n^{1-2/p})$ [BJKS04].
In this paper, we show a tight lower bound of $\Omega(n^{1-2/p}\log n)$ words
for the class of algorithms based on linear sketches, which store only a sketch
$Ax$ of input vector $x$ and some (possibly randomized) matrix $A$. We note
that all known algorithms for this problem are linear sketches.
|
1306.6302 | Solving Relational MDPs with Exogenous Events and Additive Rewards | cs.AI cs.LG | We formalize a simple but natural subclass of service domains for relational
planning problems with object-centered, independent exogenous events and
additive rewards capturing, for example, problems in inventory control.
Focusing on this subclass, we present a new symbolic planning algorithm which
is the first algorithm that has explicit performance guarantees for relational
MDPs with exogenous events. In particular, under some technical conditions, our
planning algorithm provides a monotonic lower bound on the optimal value
function. To support this algorithm we present novel evaluation and reduction
techniques for generalized first order decision diagrams, a knowledge
representation for real-valued functions over relational world states. Our
planning algorithm uses a set of focus states, which serves as a training set,
to simplify and approximate the symbolic solution, and can thus be seen to
perform learning for planning. A preliminary experimental evaluation
demonstrates the validity of our approach.
|
1306.6311 | Fast Software Polar Decoders | cs.IT math.IT | Among error-correcting codes, polar codes are the first to provably achieve
channel capacity with an explicit construction. In this work, we present
software implementations of a polar decoder that leverage the capabilities of
modern general-purpose processors to achieve an information throughput in
excess of 200 Mbps, a throughput well suited for software-defined-radio
applications. We also show that, for a similar error-correction performance,
the throughput of polar decoders both surpasses that of LDPC decoders targeting
general-purpose processors and is competitive with that of state-of-the-art
software LDPC decoders running on graphic processing units.
|
1306.6370 | Social Ranking Techniques for the Web | cs.SI cs.IR physics.soc-ph | The proliferation of social media has the potential for changing the
structure and organization of the web. In the past, scientists have looked at
the web as a large connected component to understand how the topology of
hyperlinks correlates with the quality of information contained in the page and
they proposed techniques to rank information contained in web pages. We argue
that information from web pages and network data on social relationships can be
combined to create a personalized and socially connected web. In this paper, we
look at the web as a composition of two networks, one consisting of information
in web pages and the other of personal data shared on social media web sites.
Together, they allow us to analyze how social media tunnels the flow of
information from person to person and how to use the structure of the social
network to rank, deliver, and organize information specifically for each
individual user. We validate our social ranking concepts through a ranking
experiment conducted on web pages that users shared on Google Buzz and Twitter.
|
1306.6375 | Metaheuristics in Flood Disaster Management and Risk Assessment | cs.AI | A conceptual area is divided into units or barangays, each was allowed to
evolve under a physical constraint. A risk assessment method was then used to
identify the flood risk in each community using the following risk factors: the
area's urbanized area ratio, literacy rate, mortality rate, poverty incidence,
radio/TV penetration, and state of structural and non-structural measures.
Vulnerability is defined as a weighted-sum of these components. A penalty was
imposed for reduced vulnerability. Optimization comparison was done with
MatLab's Genetic Algorithms and Simulated Annealing; results showed 'extreme'
solutions and realistic designs, for simulated annealing and genetic algorithm,
respectively.
|
1306.6378 | Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient
Projection and Krylov Subspace Techniques | cs.IT math.IT | In this paper, we propose a novel reduced-rank adaptive filtering algorithm
by blending the idea of the Krylov subspace methods with the set-theoretic
adaptive filtering framework. Unlike the existing Krylov-subspace-based
reduced-rank methods, the proposed algorithm tracks the optimal point in the
sense of minimizing the \sinq{true} mean square error (MSE) in the Krylov
subspace, even when the estimated statistics become erroneous (e.g., due to
sudden changes of environments). Therefore, compared with those existing
methods, the proposed algorithm is more suited to adaptive filtering
applications. The algorithm is analyzed based on a modified version of the
adaptive projected subgradient method (APSM). Numerical examples demonstrate
that the proposed algorithm enjoys better tracking performance than the
existing methods for the interference suppression problem in code-division
multiple-access (CDMA) systems as well as for simple system identification
problems.
|
1306.6399 | A null space analysis of the L1 synthesis method in dictionary-based
compressed sensing | cs.IT math.IT | An interesting topic in compressed sensing aims to recover signals with
sparse representations in a dictionary. Recently the performance of the
L1-analysis method has been a focus, while some fundamental problems for the
L1-synthesis method are still unsolved. For example, what are the conditions
for it to stably recover compressible signals under noise? Whether coherent
dictionaries allow the existence of sensing matrices that guarantee good
performances of the L1-synthesis method? To answer these questions, we build up
a framework for the L1-synthesis method. In particular, we propose a
dictionary-based null space property DNSP which, to the best of our knowledge,
is the first sufficient and necessary condition for the success of L1-synthesis
without measurement noise. With this new property, we show that when the
dictionary D is full spark, it cannot be too coherent otherwise the method
fails for all sensing matrices. We also prove that in the real case, DNSP is
equivalent to the stability of L1-synthesis under noise.
|
1306.6438 | Group testing algorithms: bounds and simulations | cs.IT math.IT math.PR | We consider the problem of non-adaptive noiseless group testing of $N$ items
of which $K$ are defective. We describe four detection algorithms: the COMP
algorithm of Chan et al.; two new algorithms, DD and SCOMP, which require
stronger evidence to declare an item defective; and an essentially optimal but
computationally difficult algorithm called SSS. By considering the asymptotic
rate of these algorithms with Bernoulli designs we see that DD outperforms
COMP, that DD is essentially optimal in regimes where $K \geq \sqrt N$, and
that no algorithm with a nonadaptive Bernoulli design can perform as well as
the best non-random adaptive designs when $K > N^{0.35}$. In simulations, we
see that DD and SCOMP far outperform COMP, with SCOMP very close to the optimal
SSS, especially in cases with larger $K$.
|
1306.6482 | Traffic data reconstruction based on Markov random field modeling | stat.ML cond-mat.dis-nn cs.LG | We consider the traffic data reconstruction problem. Suppose we have the
traffic data of an entire city that are incomplete because some road data are
unobserved. The problem is to reconstruct the unobserved parts of the data. In
this paper, we propose a new method to reconstruct incomplete traffic data
collected from various traffic sensors. Our approach is based on Markov random
field modeling of road traffic. The reconstruction is achieved by using
mean-field method and a machine learning method. We numerically verify the
performance of our method using realistic simulated traffic data for the real
road network of Sendai, Japan.
|
1306.6489 | A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship
Selection | cs.AI | As the education fees are becoming more expensive, more students apply for
scholarships. Consequently, hundreds and even thousands of applications need to
be handled by the sponsor. To solve the problems, some alternatives based on
several attributes (criteria) need to be selected. In order to make a decision
on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be
applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS
and Weighted Product (WP) methods is applied to select the candidates for
academic and non-academic scholarships at Universitas Islam Negeri Sunan
Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS
and Weighted Product FMADM methods can be used to select the most suitable
candidates to receive the scholarships since the preference values applied in
this method can show applicants with the highest eligibility
|
1306.6510 | Multi-Structural Signal Recovery for Biomedical Compressive Sensing | cs.IT math.IT stat.AP | Compressive sensing has shown significant promise in biomedical fields. It
reconstructs a signal from sub-Nyquist random linear measurements. Classical
methods only exploit the sparsity in one domain. A lot of biomedical signals
have additional structures, such as multi-sparsity in different domains,
piecewise smoothness, low rank, etc. We propose a framework to exploit all the
available structure information. A new convex programming problem is generated
with multiple convex structure-inducing constraints and the linear measurement
fitting constraint. With additional a priori information for solving the
underdetermined system, the signal recovery performance can be improved. In
numerical experiments, we compare the proposed method with classical methods.
Both simulated data and real-life biomedical data are used. Results show that
the newly proposed method achieves better reconstruction accuracy performance
in term of both L1 and L2 errors.
|
1306.6542 | Real-time Bidding for Online Advertising: Measurement and Analysis | cs.GT cs.CE cs.IR | The real-time bidding (RTB), aka programmatic buying, has recently become the
fastest growing area in online advertising. Instead of bulking buying and
inventory-centric buying, RTB mimics stock exchanges and utilises computer
algorithms to automatically buy and sell ads in real-time; It uses per
impression context and targets the ads to specific people based on data about
them, and hence dramatically increases the effectiveness of display
advertising. In this paper, we provide an empirical analysis and measurement of
a production ad exchange. Using the data sampled from both demand and supply
side, we aim to provide first-hand insights into the emerging new impression
selling infrastructure and its bidding behaviours, and help identifying
research and design issues in such systems. From our study, we observed that
periodic patterns occur in various statistics including impressions, clicks,
bids, and conversion rates (both post-view and post-click), which suggest
time-dependent models would be appropriate for capturing the repeated patterns
in RTB. We also found that despite the claimed second price auction, the first
price payment in fact is accounted for 55.4% of total cost due to the
arrangement of the soft floor price. As such, we argue that the setting of soft
floor price in the current RTB systems puts advertisers in a less favourable
position. Furthermore, our analysis on the conversation rates shows that the
current bidding strategy is far less optimal, indicating the significant needs
for optimisation algorithms incorporating the facts such as the temporal
behaviours, the frequency and recency of the ad displays, which have not been
well considered in the past.
|
1306.6572 | Stochastic Optimal Control as Non-equilibrium Statistical Mechanics:
Calculus of Variations over Density and Current | cond-mat.stat-mech cs.SY math-ph math.MP math.OC | In Stochastic Optimal Control (SOC) one minimizes the average cost-to-go,
that consists of the cost-of-control (amount of efforts), cost-of-space (where
one wants the system to be) and the target cost (where one wants the system to
arrive), for a system participating in forced and controlled Langevin dynamics.
We extend the SOC problem by introducing an additional cost-of-dynamics,
characterized by a vector potential. We propose derivation of the generalized
gauge-invariant Hamilton-Jacobi-Bellman equation as a variation over density
and current, suggest hydrodynamic interpretation and discuss examples, e.g.,
ergodic control of a particle-within-a-circle, illustrating non-equilibrium
space-time complexity.
|
1306.6578 | Multiphysics simulation of corona discharge induced ionic wind | physics.comp-ph cs.CE physics.flu-dyn | Ionic wind devices or electrostatic fluid accelerators are becoming of
increasing interest as tools for thermal management, in particular for
semiconductor devices. In this work, we present a numerical model for
predicting the performance of such devices, whose main benefit is the ability
to accurately predict the amount of charge injected at the corona electrode.
Our multiphysics numerical model consists of a highly nonlinear strongly
coupled set of PDEs including the Navier-Stokes equations for fluid flow,
Poisson's equation for electrostatic potential, charge continuity and heat
transfer equations. To solve this system we employ a staggered solution
algorithm that generalizes Gummel's algorithm for charge transport in
semiconductors. Predictions of our simulations are validated by comparison with
experimental measurements and are shown to closely match. Finally, our
simulation tool is used to estimate the effectiveness of the design of an
electrohydrodynamic cooling apparatus for power electronics applications.
|
1306.6595 | Altmetrics: New Indicators for Scientific Communication in Web 2.0 | cs.DL cs.SI physics.soc-ph | In this paper we review the socalled altmetrics or alternative metrics. This
concept raises from the development of new indicators based on Web 2.0, for the
evaluation of the research and academic activity. The basic assumption is that
variables such as mentions in blogs, number of twits or of researchers
bookmarking a research paper for instance, may be legitimate indicators for
measuring the use and impact of scientific publications. In this sense, these
indicators are currently the focus of the bibliometric community and are being
discussed and debated. We describe the main platforms and indicators and we
analyze as a sample the Spanish research output in Communication Studies.
Comparing traditional indicators such as citations with these new indicators.
The results show that the most cited papers are also the ones with a highest
impact according to the altmetrics. We conclude pointing out the main
shortcomings these metrics present and the role they may play when measuring
the research impact through 2.0 platforms.
|
1306.6649 | Measurements of collective machine intelligence | cs.AI cs.MA | Independent from the still ongoing research in measuring individual
intelligence, we anticipate and provide a framework for measuring collective
intelligence. Collective intelligence refers to the idea that several
individuals can collaborate in order to achieve high levels of intelligence. We
present thus some ideas on how the intelligence of a group can be measured and
simulate such tests. We will however focus here on groups of artificial
intelligence agents (i.e., machines). We will explore how a group of agents is
able to choose the appropriate problem and to specialize for a variety of
tasks. This is a feature which is an important contributor to the increase of
intelligence in a group (apart from the addition of more agents and the
improvement due to common decision making). Our results reveal some interesting
results about how (collective) intelligence can be modeled, about how
collective intelligence tests can be designed and about the underlying dynamics
of collective intelligence. As it will be useful for our simulations, we
provide also some improvements of the threshold allocation model originally
used in the area of swarm intelligence but further generalized here.
|
1306.6659 | Millimeter Wave Beamforming for Wireless Backhaul and Access in Small
Cell Networks | cs.IT math.IT | Recently, there has been considerable interest in new tiered network cellular
architectures, which would likely use many more cell sites than found today.
Two major challenges will be i) providing backhaul to all of these cells and
ii) finding efficient techniques to leverage higher frequency bands for mobile
access and backhaul. This paper proposes the use of outdoor millimeter wave
communications for backhaul networking between cells and mobile access within a
cell. To overcome the outdoor impairments found in millimeter wave propagation,
this paper studies beamforming using large arrays. However, such systems will
require narrow beams, increasing sensitivity to movement caused by pole sway
and other environmental concerns. To overcome this, we propose an efficient
beam alignment technique using adaptive subspace sampling and hierarchical beam
codebooks. A wind sway analysis is presented to establish a notion of beam
coherence time. This highlights a previously unexplored tradeoff between array
size and wind-induced movement. Generally, it is not possible to use larger
arrays without risking a corresponding performance loss from wind-induced beam
misalignment. The performance of the proposed alignment technique is analyzed
and compared with other search and alignment methods. The results show
significant performance improvement with reduced search time.
|
1306.6670 | Towards a better insight of RDF triples Ontology-guided Storage system
abilities | cs.DB | The vision of the Semantic Web is becoming a reality with billions of RDF
triples being distributed over multiple queryable end-points (e.g. Linked
Data). Although there has been a body of work on RDF triples persistent
storage, it seems that, considering reasoning dependent queries, the problem of
providing an efficient, in terms of performance, scalability and data
redundancy, partitioning of the data is still open. In regards to recent data
partitioning studies, it seems reasonable to think that data partitioning
should be guided considering several directions (e.g. ontology, data,
application queries). This paper proposes several contributions: describe an
overview of what a road map for data partitioning for RDF data efficient and
persistent storage should contain, present some preliminary results and
analysis on the particular case of ontology-guided (property hierarchy)
partitioning and finally introduce a set of semantic query rewriting rules to
support querying RDF data needing OWL inferences
|
1306.6671 | Extended Subspace Error Localization for Rate-Adaptive Distributed
Source Coding | cs.IT math.IT | A subspace-based approach for rate-adaptive distributed source coding (DSC)
based on discrete Fourier transform (DFT) codes is developed. Punctured DFT
codes can be used to implement rate-adaptive source coding, however they
perform poorly after even moderate puncturing since the performance of the
subspace error localization degrades severely. The proposed subspace-based
error localization extends and improves the existing one, based on additional
syndrome, and is naturally suitable for rate-adaptive distributed source coding
architecture.
|
1306.6675 | Next generation input-output data format for HEP using Google's protocol
buffers | cs.CE cs.MS hep-ph | We propose a data format for Monte Carlo (MC) events, or any structural data,
including experimental data, in a compact binary form using variable-size
integer encoding as implemented in the Google's Protocol Buffers package. This
approach is implemented in the so-called ProMC library which produces smaller
file sizes for MC records compared to the existing input-output libraries used
in high-energy physics (HEP). Other important features are a separation of
abstract data layouts from concrete programming implementations,
self-description and random access. Data stored in ProMC files can be written,
read and manipulated in a number of programming languages, such C++, Java and
Python.
|
1306.6709 | A Survey on Metric Learning for Feature Vectors and Structured Data | cs.LG cs.AI stat.ML | The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.
|
1306.6726 | A Novel Active Contour Model for Texture Segmentation | cs.CV | Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images.
|
1306.6734 | A novel ER model to relational model transformation algorithm for
semantically clear high quality database design | cs.DB | Conceptual modelling using the entity relationship (ER) model has been widely
used for database design for a long period of time. However, studies indicate
that creating a satisfactory relational model representation from an ER model
is uncertain due to the insufficiencies both in the transformation methods used
and in the relational model itself. In an effort to solve the issue the
original ER notation has been modified, and accordingly, a new transformation
algorithm has been developed. This paper presents the proposed transformation
algorithm. Using a real world example it shows how the algorithm can be applied
in practice. The paper also discusses how to validate the resulted database and
reclaim the information that it represents.
|
1306.6735 | An Analysis of the DS-CDMA Cellular Uplink for Arbitrary and Constrained
Topologies | cs.IT math.IT | A new analysis is presented for the direct-sequence code-division multiple
access (DS-CDMA) cellular uplink. For a given network topology, closed-form
expressions are found for the outage probability and rate of each uplink in the
presence of path-dependent Nakagami fading and shadowing. The topology may be
arbitrary or modeled by a random spatial distribution with a fixed number of
base stations and mobiles placed over a finite area. The analysis is more
detailed and accurate than existing ones and facilitates the resolution of
network design issues including the influence of the minimum base-station
separation, the role of the spreading factor, and the impact of various
power-control and rate-control policies. It is shown that once power control is
established, the rate can be allocated according to a fixed-rate or
variable-rate policy with the objective of either meeting an outage constraint
or maximizing throughput. An advantage of variable-rate power control is that
it allows an outage constraint to be enforced on every uplink, which is
impossible when a fixed rate is used throughout the network.
|
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