id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1312.2903 | The lower tail of random quadratic forms, with applications to ordinary
least squares and restricted eigenvalue properties | math.PR cs.IT math.IT math.ST stat.TH | Finite sample properties of random covariance-type matrices have been the
subject of much research. In this paper we focus on the "lower tail" of such a
matrix, and prove that it is subgaussian under a simple fourth moment
assumption on the one-dimensional marginals of the random vectors. A similar
result holds for more general sums of random positive semidefinite matrices,
and the (relatively simple) proof uses a variant of the so-called PAC-Bayesian
method for bounding empirical processes.
We give two applications of the main result. In the first one we obtain a new
finite-sample bound for ordinary least squares estimator in linear regression
with random design. Our result is model-free, requires fairly weak moment
assumptions and is almost optimal. Our second application is to bounding
restricted eigenvalue constants of certain random ensembles with "heavy tails".
These constants are important in the analysis of problems in Compressed Sensing
and High Dimensional Statistics, where one recovers a sparse vector from a
small umber of linear measurements. Our result implies that heavy tails still
allow for the fast recovery rates found in efficient methods such as the LASSO
and the Dantzig selector. Along the way we strengthen, with a fairly short
argument, a recent result of Rudelson and Zhou on the restricted eigenvalue
property.
|
1312.2919 | Win-Move is Coordination-Free (Sometimes) | cs.DB | In a recent paper by Hellerstein [15], a tight relationship was conjectured
between the number of strata of a Datalog${}^\neg$ program and the number of
"coordination stages" required for its distributed computation. Indeed, Ameloot
et al. [9] showed that a query can be computed by a coordination-free
relational transducer network iff it is monotone, thus answering in the
affirmative a variant of Hellerstein's CALM conjecture, based on a particular
definition of coordination-free computation. In this paper, we present three
additional models for declarative networking. In these variants, relational
transducers have limited access to the way data is distributed. This variation
allows transducer networks to compute more queries in a coordination-free
manner: e.g., a transducer can check whether a ground atom $A$ over the input
schema is in the "scope" of the local node, and then send either $A$ or $\neg
A$ to other nodes.
We show the surprising result that the query given by the well-founded
semantics of the unstratifiable win-move program is coordination-free in some
of the models we consider. We also show that the original transducer network
model [9] and our variants form a strict hierarchy of classes of
coordination-free queries. Finally, we identify different syntactic fragments
of Datalog${}^{\neg\neg}_{\forall}$, called semi-monotone programs, which can
be used as declarative network programming languages, whose distributed
computation is guaranteed to be eventually consistent and coordination-free.
|
1312.2936 | Active Player Modelling | cs.LG | We argue for the use of active learning methods for player modelling. In
active learning, the learning algorithm chooses where to sample the search
space so as to optimise learning progress. We hypothesise that player modelling
based on active learning could result in vastly more efficient learning, but
will require big changes in how data is collected. Some example active player
modelling scenarios are described. A particular form of active learning is also
equivalent to an influential formalisation of (human and machine) curiosity,
and games with active learning could therefore be seen as being curious about
the player. We further hypothesise that this form of curiosity is symmetric,
and therefore that games that explore their players based on the principles of
active learning will turn out to select game configurations that are
interesting to the player that is being explored.
|
1312.2983 | An Efficient Clustering Algorithm for Device-to-Device Assisted Virtual
MIMO | cs.IT math.IT | In this paper, the utilization of mobile devices (MDs) as decode-and-forward
relays in a device-to-device assisted virtual MIMO (VMIMO) system is studied.
Single antenna MDs are randomly distributed on a 2D plane according to a
Poisson point process, and only a subset of them are sources leaving other idle
MDs available to assist them (relays). Our goal is to develop an efficient
algorithm to cluster each source with a subset of available relays to form a
VMIMO system under a limited feedback assumption. We first show that the NP-
hard optimization problem of precoding in our scenario can be approximately
solved by semidefinite relaxation. We investigate a special case with a single
source and analytically derive an upper bound on the average spectral
efficiency of the VMIMO system. Then, we propose an optimal greedy algorithm
that achieves this bound. We further exploit these results to obtain a
polynomial time clustering algorithm for the general case with multiple
sources. Finally, numerical simulations are performed to compare the
performance of our algorithm with that of an exhaustive clustering algorithm,
and it shown that these numerical results corroborate the efficiency of our
algorithm.
|
1312.2984 | Synchrophasor monitoring of single line outages via area angle and
susceptance | cs.SY | The area angle is a scalar measure of power system area stress that responds
to line outages within the area and is a combination of synchrophasor
measurements of voltage angles around the border of the area. Both idealized
and practical examples are given to show that the variation of the area angle
for single line outages can be approximately related to changes in the overall
susceptance of the area and the line outage severity.
|
1312.2986 | Notes on discrepancy in the pairwise comparisons method | cs.DM cs.IR | The pairwise comparisons method is a convenient tool used when the relative
order among different concepts (alternatives) needs to be determined. One
popular implementation of the method is based on solving an eigenvalue problem
for the pairwise comparisons matrix. In such cases the ranking result the
principal eigenvector of the pairwise comparison matrix is adopted, whilst the
eigenvalue is used to determine the index of inconsistency. A lot of research
has been devoted to the critical analysis of the eigenvalue based approach. One
of them is the work (Bana e Costa and Vansnick, 2008). In their work authors
define the conditions of order preservation (COP) and show that even for a
sufficiently consistent pairwise comparisons matrices, this condition can not
be met. The present work defines a more precise criteria for determining when
the COP is met. To formulate the criteria a discrepancy factor is used
describing how far the input to the ranking procedure is from the ranking
result.
|
1312.2988 | Protein Contact Prediction by Integrating Joint Evolutionary Coupling
Analysis and Supervised Learning | q-bio.QM cs.LG math.OC q-bio.BM stat.ML | Protein contacts contain important information for protein structure and
functional study, but contact prediction from sequence remains very
challenging. Both evolutionary coupling (EC) analysis and supervised machine
learning methods are developed to predict contacts, making use of different
types of information, respectively. This paper presents a group graphical lasso
(GGL) method for contact prediction that integrates joint multi-family EC
analysis and supervised learning. Different from existing single-family EC
analysis that uses residue co-evolution information in only the target protein
family, our joint EC analysis uses residue co-evolution in both the target
family and its related families, which may have divergent sequences but similar
folds. To implement joint EC analysis, we model a set of related protein
families using Gaussian graphical models (GGM) and then co-estimate their
precision matrices by maximum-likelihood, subject to the constraint that the
precision matrices shall share similar residue co-evolution patterns. To
further improve the accuracy of the estimated precision matrices, we employ a
supervised learning method to predict contact probability from a variety of
evolutionary and non-evolutionary information and then incorporate the
predicted probability as prior into our GGL framework. Experiments show that
our method can predict contacts much more accurately than existing methods, and
that our method performs better on both conserved and family-specific contacts.
|
1312.2990 | Efficient Lineage for SUM Aggregate Queries | cs.DB | AI systems typically make decisions and find patterns in data based on the
computation of aggregate and specifically sum functions, expressed as queries,
on data's attributes. This computation can become costly or even inefficient
when these queries concern the whole or big parts of the data and especially
when we are dealing with big data. New types of intelligent analytics require
also the explanation of why something happened. In this paper we present a
randomised algorithm that constructs a small summary of the data, called
Aggregate Lineage, which can approximate well and explain all sums with large
values in time that depends only on its size. The size of Aggregate Lineage is
practically independent on the size of the original data. Our algorithm does
not assume any knowledge on the set of sum queries to be approximated.
|
1312.3005 | One Billion Word Benchmark for Measuring Progress in Statistical
Language Modeling | cs.CL | We propose a new benchmark corpus to be used for measuring progress in
statistical language modeling. With almost one billion words of training data,
we hope this benchmark will be useful to quickly evaluate novel language
modeling techniques, and to compare their contribution when combined with other
advanced techniques. We show performance of several well-known types of
language models, with the best results achieved with a recurrent neural network
based language model. The baseline unpruned Kneser-Ney 5-gram model achieves
perplexity 67.6; a combination of techniques leads to 35% reduction in
perplexity, or 10% reduction in cross-entropy (bits), over that baseline.
The benchmark is available as a code.google.com project; besides the scripts
needed to rebuild the training/held-out data, it also makes available
log-probability values for each word in each of ten held-out data sets, for
each of the baseline n-gram models.
|
1312.3020 | Sparse Allreduce: Efficient Scalable Communication for Power-Law Data | cs.DC cs.AI cs.MS | Many large datasets exhibit power-law statistics: The web graph, social
networks, text data, click through data etc. Their adjacency graphs are termed
natural graphs, and are known to be difficult to partition. As a consequence
most distributed algorithms on these graphs are communication intensive. Many
algorithms on natural graphs involve an Allreduce: a sum or average of
partitioned data which is then shared back to the cluster nodes. Examples
include PageRank, spectral partitioning, and many machine learning algorithms
including regression, factor (topic) models, and clustering. In this paper we
describe an efficient and scalable Allreduce primitive for power-law data. We
point out scaling problems with existing butterfly and round-robin networks for
Sparse Allreduce, and show that a hybrid approach improves on both.
Furthermore, we show that Sparse Allreduce stages should be nested instead of
cascaded (as in the dense case). And that the optimum throughput Allreduce
network should be a butterfly of heterogeneous degree where degree decreases
with depth into the network. Finally, a simple replication scheme is introduced
to deal with node failures. We present experiments showing significant
improvements over existing systems such as PowerGraph and Hadoop.
|
1312.3035 | Heat kernel coupling for multiple graph analysis | cs.CV | In this paper, we introduce heat kernel coupling (HKC) as a method of
constructing multimodal spectral geometry on weighted graphs of different size
without vertex-wise bijective correspondence. We show that Laplacian averaging
can be derived as a limit case of HKC, and demonstrate its applications on
several problems from the manifold learning and pattern recognition domain.
|
1312.3041 | Cross-Layer MIMO Transceiver Optimization for Multimedia Streaming in
Interference Networks | cs.IT cs.MM math.IT | In this paper, we consider dynamic precoder/decorrelator optimization for
multimedia streaming in MIMO interference networks. We propose a truly
cross-layer framework in the sense that the optimization objective is the
application level performance metrics for multimedia streaming, namely the
playback interruption and buffer overflow probabilities. The optimization
variables are the MIMO precoders/decorrelators at the transmitters and the
receivers, which are adaptive to both the instantaneous channel condition and
the playback queue length. The problem is a challenging multi-dimensional
stochastic optimization problem and brute-force solution has exponential
complexity. By exploiting the underlying timescale separation and special
structure in the problem, we derive a closed-form approximation of the value
function based on continuous time perturbation. Using this approximation, we
propose a low complexity dynamic MIMO precoder/decorrelator control algorithm
by solving an equivalent weighted MMSE problem. We also establish the technical
conditions for asymptotic optimality of the low complexity control algorithm.
Finally, the proposed scheme is compared with various baselines through
simulations and it is shown that significant performance gain can be achieved.
|
1312.3048 | Deterministic and stochastic analysis of distributed order systems using
operational matrix | cs.SY | The fractional order system, which is described by the fractional order
derivative and integral, has been studied in many engineering areas. Recently,
the concept of fractional order has been generalized to the distributed order
concept, which is a parallel connection of fractional order integrals and
derivatives taken to the infinitesimal limit in delta order. On the other hand,
there are very few numerical methods available for the analysis of distributed
order systems, particularly under stochastic forcing. This paper first proposes
a numerical scheme for analyzing the behavior of a SISO linear system with a
single term distributed order differentiator/integrator using an operational
matrix in the time domain under both deterministic and random forcing. To
assess the stochastic distributed order system, the existing Monte-Carlo,
polynomial chaos and frequency methods are first adopted to the stochastic
distributed order system for comparison. The numerical examples demonstrate the
accuracy and computational efficiency of the proposed method for analyzing
stochastic distributed order systems.
|
1312.3060 | Representing Knowledge Base into Database for WAP and Web-based Expert
System | cs.AI cs.CY | Expert System is developed as consulting service for users spread or public
requires affordable access. The Internet has become a medium for such services,
but presence of mobile devices make the access becomes more widespread by
utilizing mobile web and WAP (Wireless Application Protocol). Applying expert
systems applications over the web and WAP requires a knowledge base
representation that can be accessed simultaneously. This paper proposes single
database to accommodate the knowledge representation with decision tree mapping
approach. Because of the database exist, consulting application through both
web and WAP can access it to provide expert system services options for more
affordable for public.
|
1312.3061 | Fast Approximate $K$-Means via Cluster Closures | cs.CV | $K$-means, a simple and effective clustering algorithm, is one of the most
widely used algorithms in multimedia and computer vision community. Traditional
$k$-means is an iterative algorithm---in each iteration new cluster centers are
computed and each data point is re-assigned to its nearest center. The cluster
re-assignment step becomes prohibitively expensive when the number of data
points and cluster centers are large.
In this paper, we propose a novel approximate $k$-means algorithm to greatly
reduce the computational complexity in the assignment step. Our approach is
motivated by the observation that most active points changing their cluster
assignments at each iteration are located on or near cluster boundaries. The
idea is to efficiently identify those active points by pre-assembling the data
into groups of neighboring points using multiple random spatial partition
trees, and to use the neighborhood information to construct a closure for each
cluster, in such a way only a small number of cluster candidates need to be
considered when assigning a data point to its nearest cluster. Using complexity
analysis, image data clustering, and applications to image retrieval, we show
that our approach out-performs state-of-the-art approximate $k$-means
algorithms in terms of clustering quality and efficiency.
|
1312.3062 | Fast Neighborhood Graph Search using Cartesian Concatenation | cs.CV | In this paper, we propose a new data structure for approximate nearest
neighbor search. This structure augments the neighborhood graph with a bridge
graph. We propose to exploit Cartesian concatenation to produce a large set of
vectors, called bridge vectors, from several small sets of subvectors. Each
bridge vector is connected with a few reference vectors near to it, forming a
bridge graph. Our approach finds nearest neighbors by simultaneously traversing
the neighborhood graph and the bridge graph in the best-first strategy. The
success of our approach stems from two factors: the exact nearest neighbor
search over a large number of bridge vectors can be done quickly, and the
reference vectors connected to a bridge (reference) vector near the query are
also likely to be near the query. Experimental results on searching over large
scale datasets (SIFT, GIST and HOG) show that our approach outperforms
state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The
combination of our approach with the IVFADC system also shows superior
performance over the BIGANN dataset of $1$ billion SIFT features compared with
the best previously published result.
|
1312.3092 | A Low-Complexity Detector for Memoryless Polarization-Multiplexed
Fiber-Optical Channels | cs.IT math.IT | A low-complexity detector is introduced for polarization-multiplexed M-ary
phase shift keying modulation in a fiber-optical channel impaired by nonlinear
phase noise, generalizing a previous result by Lau and Kahn for
single-polarization signals. The proposed detector uses phase compensation
based on both received signal amplitudes in conjunction with simple
straight-line rather than four-dimensional maximum-likelihood decision
boundaries.
|
1312.3139 | Efficiency of attack strategies on complex model and real-world networks | physics.soc-ph cs.SI physics.comp-ph | We investigated the efficiency of attack strategies to network nodes when
targeting several complex model and real-world networks. We tested 5 attack
strategies, 3 of which were introduced in this work for the first time, to
attack 3 model (Erdos and Renyi, Barabasi and Albert preferential attachment
network, and scale-free network configuration models) and 3 real networks
(Gnutella peer-to-peer network, email network of the University of Rovira i
Virgili, and immunoglobulin interaction network). Nodes were removed
sequentially according to the importance criterion defined by the attack
strategy. We used the size of the largest connected component (LCC) as a
measure of network damage. We found that the efficiency of attack strategies
(fraction of nodes to be deleted for a given reduction of LCC size) depends on
the topology of the network, although attacks based on the number of
connections of a node and betweenness centrality were often the most efficient
strategies. Sequential deletion of nodes in decreasing order of betweenness
centrality was the most efficient attack strategy when targeting real-world
networks. In particular for networks with power-law degree distribution, we
observed that most efficient strategy change during the sequential removal of
nodes.
|
1312.3168 | Semantic Types, Lexical Sorts and Classifiers | cs.CL | We propose a cognitively and linguistically motivated set of sorts for
lexical semantics in a compositional setting: the classifiers in languages that
do have such pronouns. These sorts are needed to include lexical considerations
in a semantical analyser such as Boxer or Grail. Indeed, all proposed lexical
extensions of usual Montague semantics to model restriction of selection,
felicitous and infelicitous copredication require a rich and refined type
system whose base types are the lexical sorts, the basis of the many-sorted
logic in which semantical representations of sentences are stated. However,
none of those approaches define precisely the actual base types or sorts to be
used in the lexicon. In this article, we shall discuss some of the options
commonly adopted by researchers in formal lexical semantics, and defend the
view that classifiers in the languages which have such pronouns are an
appealing solution, both linguistically and cognitively motivated.
|
1312.3194 | Error-Correcting Regenerating and Locally Repairable Codes via
Rank-Metric Codes | cs.IT math.IT | This paper presents and analyzes a novel concatenated coding scheme for
enabling error resilience in two distributed storage settings: one being
storage using existing regenerating codes and the second being storage using
locally repairable codes. The concatenated coding scheme brings together a
maximum rank distance (MRD) code as an outer code and either a globally
regenerating or a locally repairable code as an inner code. Also, error
resilience for combination of locally repairable codes with regenerating codes
is considered. This concatenated coding system is designed to handle two
different types of adversarial errors: the first type includes an adversary
that can replace the content of an affected node only once; while the second
type studies an adversary that is capable of polluting data an unbounded number
of times. The paper establishes an upper bound on the resilience capacity for a
locally repairable code and proves that this concatenated coding coding scheme
attains the upper bound on resilience capacity for the first type of adversary.
Further, the paper presents mechanisms that combine the presented concatenated
coding scheme with subspace signatures to achieve error resilience for the
second type of errors.
|
1312.3198 | Secrecy Capacity Scaling in Large Cooperative Wireless Networks | cs.IT cs.CR math.IT | We investigate large wireless networks subject to security constraints. In
contrast to point-to-point, interference-limited communications considered in
prior works, we propose active cooperative relaying based schemes. We consider
a network with $n_l$ legitimate nodes, $n_e$ eavesdroppers, and path loss
exponent $\alpha\geq 2$. As long as $n_e^2(\log(n_e))^{\gamma}=o(n_l)$, for
some positive $\gamma$, we show one can obtain unbounded secure aggregate rate.
This means zero-cost secure communication, given fixed total power constraint
for the entire network. We achieve this result through (i) the source using
Wyner randomized encoder and a serial (multi-stage) block Markov scheme, to
cooperate with the relays and (ii) the relays acting as a virtual multi-antenna
to apply beamforming against the eavesdroppers. Our simpler parallel
(two-stage) relaying scheme can achieve the same unbounded secure aggregate
rate when
$n_e^{\frac{\alpha}{2}+1}(\log(n_e))^{\gamma+\delta(\frac{\alpha}{2}+1)}=o(n_l)$
holds, for some positive $\gamma,\delta$. Finally, we study the improvement (to
the detriment of legitimate nodes) the eavesdroppers achieve in terms of the
information leakage rate in a large cooperative network in case of collusion.
We show that again the zero-cost secure communication is possible, if
$n_e^{(2+\frac{2}{\alpha})}(\log n_e)^{\gamma}=o(n_l)$ holds, for some positive
$\gamma$; i.e., in case of collusion slightly fewer eavesdroppers can be
tolerated compared to the non-colluding case.
|
1312.3199 | Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral
Domain Optical Coherence Tomography | cs.CV | Purpose. This study was conducted to determine the thickness map of eleven
retinal layers in normal subjects by spectral domain optical coherence
tomography (SD-OCT) and evaluate their association with sex and age. Methods.
Mean regional retinal thickness of 11 retinal layers were obtained by automatic
three-dimensional diffusion-map-based method in 112 normal eyes of 76 Iranian
subjects. Results. The thickness map of central foveal area in layer 1, 3, and
4 displayed the minimum thickness (P<0.005 for all). Maximum thickness was
observed in nasal to the fovea of layer 1 (P<0.001) and in a circular pattern
in the parafoveal retinal area of layers 2, 3 and 4 and in central foveal area
of layer 6 (P<0.001). Temporal and inferior quadrants of the total retinal
thickness and most of other quadrants of layer 1 were significantly greater in
the men than in the women. Surrounding eight sectors of total retinal thickness
and a limited number of sectors in layer 1 and 4 significantly correlated with
age. Conclusion. SD-OCT demonstrated the three-dimensional thickness
distribution of retinal layers in normal eyes. Thickness of layers varied with
sex and age and in different sectors. These variables should be considered
while evaluating macular thickness.
|
1312.3200 | Constrained Colluding Eavesdroppers: An Information-Theoretic Model | cs.IT cs.CR math.IT | We study the secrecy capacity in the vicinity of colluding eavesdroppers.
Contrary to the perfect collusion assumption in previous works, our new
information-theoretic model considers constraints in collusion. We derive the
achievable secure rates (lower bounds on the perfect secrecy capacity), both
for the discrete memoryless and Gaussian channels. We also compare the proposed
rates to the non-colluding and perfect colluding cases.
|
1312.3222 | Mobile Robots in Teaching Programming for IT Engineers and its Effects | cs.CY cs.RO | In this paper the new methods and devices introduced into the learning
process of programming for IT engineers at our college is described. Based on
our previous research results we supposed that project methods and some new
devices can reduce programming problems during the first term. These problems
are rooted in the difficulties of abstract thinking and they can cause the
decrease of programming self-concept and other learning motives. We redesigned
the traditional learning environment. As a constructive approach project method
was used. Our students worked in groups of two or three; small problems were
solved after every lesson. In the problem solving process students use
programmable robots (e.g. Surveyor, LEGO NXT and RCX). They had to plan their
program, solve some technical problems and test their solution. The usability
of mobile robots in the learning process and the short-term efficiency of our
teaching method were checked with a control group after a semester (n = 149).
We examined the effects on our students' programming skills and on their
motives, mainly on their attitudes and programming self-concept. After a
two-year-long period we could measure some positive long-term effects.
|
1312.3234 | Communicability reveals a transition to coordinated behavior in
multiplex networks | physics.soc-ph cs.SI | We analyse the flow of information in multiplex networks by means of the
communicability function. First, we generalize this measure from its definition
from simple graphs to multiplex networks. Then, we study its relevance for the
analysis of real-world systems by studying a social multiplex where information
flows using formal/informal channels and an air transportation system where the
layers represent different air companies. Accordingly, the communicability,
which is essential for the good performance of these complex systems, emerges
at a systemic operation point in the multiplex where the performance of the
layers operates in a coordinated way very differently from the state
represented by a collection of unconnected networks.
|
1312.3240 | Associative embeddings for large-scale knowledge transfer with
self-assessment | cs.CV | We propose a method for knowledge transfer between semantically related
classes in ImageNet. By transferring knowledge from the images that have
bounding-box annotations to the others, our method is capable of automatically
populating ImageNet with many more bounding-boxes and even pixel-level
segmentations. The underlying assumption that objects from semantically related
classes look alike is formalized in our novel Associative Embedding (AE)
representation. AE recovers the latent low-dimensional space of appearance
variations among image windows. The dimensions of AE space tend to correspond
to aspects of window appearance (e.g. side view, close up, background). We
model the overlap of a window with an object using Gaussian Processes (GP)
regression, which spreads annotation smoothly through AE space. The
probabilistic nature of GP allows our method to perform self-assessment, i.e.
assigning a quality estimate to its own output. It enables trading off the
amount of returned annotations for their quality. A large scale experiment on
219 classes and 0.5 million images demonstrates that our method outperforms
state-of-the-art methods and baselines for both object localization and
segmentation. Using self-assessment we can automatically return bounding-box
annotations for 30% of all images with high localization accuracy (i.e.~73%
average overlap with ground-truth).
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1312.3248 | On the Complexity of Mining Itemsets from the Crowd Using Taxonomies | cs.DB cs.CC cs.IR | We study the problem of frequent itemset mining in domains where data is not
recorded in a conventional database but only exists in human knowledge. We
provide examples of such scenarios, and present a crowdsourcing model for them.
The model uses the crowd as an oracle to find out whether an itemset is
frequent or not, and relies on a known taxonomy of the item domain to guide the
search for frequent itemsets. In the spirit of data mining with oracles, we
analyze the complexity of this problem in terms of (i) crowd complexity, that
measures the number of crowd questions required to identify the frequent
itemsets; and (ii) computational complexity, that measures the computational
effort required to choose the questions. We provide lower and upper complexity
bounds in terms of the size and structure of the input taxonomy, as well as the
size of a concise description of the output itemsets. We also provide
constructive algorithms that achieve the upper bounds, and consider more
efficient variants for practical situations.
|
1312.3251 | Towards The Development of a Bishnupriya Manipuri Corpus | cs.CL | For any deep computational processing of language we need evidences, and one
such set of evidences is corpus. This paper describes the development of a
text-based corpus for the Bishnupriya Manipuri language. A Corpus is considered
as a building block for any language processing tasks. Due to the lack of
awareness like other Indian languages, it is also studied less frequently. As a
result the language still lacks a good corpus and basic language processing
tools. As per our knowledge this is the first effort to develop a corpus for
Bishnupriya Manipuri language.
|
1312.3258 | Implicit Sensitive Text Summarization based on Data Conveyed by
Connectives | cs.CL | So far and trying to reach human capabilities, research in automatic
summarization has been based on hypothesis that are both enabling and limiting.
Some of these limitations are: how to take into account and reflect (in the
generated summary) the implicit information conveyed in the text, the author
intention, the reader intention, the context influence, the general world
knowledge. Thus, if we want machines to mimic human abilities, then they will
need access to this same large variety of knowledge. The implicit is affecting
the orientation and the argumentation of the text and consequently its summary.
Most of Text Summarizers (TS) are processing as compressing the initial data
and they necessarily suffer from information loss. TS are focusing on features
of the text only, not on what the author intended or why the reader is reading
the text. In this paper, we address this problem and we present a system
focusing on acquiring knowledge that is implicit. We principally spotlight the
implicit information conveyed by the argumentative connectives such as: but,
even, yet and their effect on the summary.
|
1312.3263 | Stable Embedding of Grassmann Manifold via Gaussian Random matrices | cs.IT math.IT | In this paper, we explore a volume-based stable embedding of
multi-dimensional signals based on Grassmann manifold, via Gaussian random
measurement matrices. The Grassmann manifold is a topological space in which
each point is a linear vector subspace, and is widely regarded as an ideal
model for multi-dimensional signals. In this paper, we formulate the linear
subspace spanned by multi-dimensional signal vectors as points on the Grassmann
manifold, and use the volume and the product of sines of principal angles (also
known as the product of principal sines) as the generalized norm and distance
measure for the space of Grassmann manifold. We prove a volume-preserving
embedding property for points on the Grassmann manifold via Gaussian random
measurement matrices, i.e., the volumes of all parallelotopes from a finite set
in Grassmann manifold are preserved upon compression. This volume-preserving
embedding property is a multi-dimensional generalization of the conventional
stable embedding properties, which only concern the approximate preservation of
lengths of vectors in certain unions of subspaces. Additionally, we use the
volume-preserving embedding property to explore the stable embedding effect on
a generalized distance measure of Grassmann manifold induced from volume. It is
proved that the generalized distance measure, i.e., the product of principal
sines between different points on the Grassmann manifold, is well preserved in
the compressed domain via Gaussian random measurement matrices.Numerical
simulations are also provided for validation.
|
1312.3269 | Power Scheduling of Kalman Filtering in Wireless Sensor Networks with
Data Packet Drops | cs.SY | For a wireless sensor network (WSN) with a large number of low-cost,
battery-driven, multiple transmission power leveled sensor nodes of limited
transmission bandwidth, then conservation of transmission resources (power and
bandwidth) is of paramount importance. Towards this end, this paper considers
the problem of power scheduling of Kalman filtering for general linear
stochastic systems subject to data packet drops (over a packet-dropping
wireless network). The transmission of the acquired measurement from the sensor
to the remote estimator is realized by sequentially transmitting every single
component of the measurement to the remote estimator in one time period. The
sensor node decides separately whether to use a high or low transmission power
to communicate every component to the estimator across a packet-dropping
wireless network based on the rule that promotes the power scheduling with the
least impact on the estimator mean squared error. Under the customary
assumption that the predicted density is (approximately) Gaussian, leveraging
the statistical distribution of sensor data, the mechanism of power scheduling,
the wireless network effect and the received data, the minimum mean squared
error estimator is derived. By investigating the statistical convergence
properties of the estimation error covariance, we establish, for general linear
systems, both the sufficient condition and the necessary condition guaranteeing
the stability of the estimator.
|
1312.3304 | The Effect of Eavesdropper's Statistics in Experimental Wireless
Secret-Key Generation | cs.IT math.IT | This paper investigates the role of the eavesdropper's statistics in the
implementation of a practical secret-key generation system. We carefully
conduct the information-theoretic analysis of a secret-key generation system
from wireless channel gains measured with software-defined radios. In
particular, we show that it is inaccurate to assume that the eavesdropper gets
no information because of decorrelation with distance. We also provide a bound
for the achievable secret-key rate in the finite key-length regime that takes
into account the presence of correlated eavesdropper's observations. We
evaluate this bound with our experimental gain measurements to show that
operating with a finite number of samples incurs a loss in secret-key rate on
the order of 20%.
|
1312.3368 | New Codes on Graphs Constructed by Connecting Spatially Coupled Chains | cs.IT math.IT | A novel code construction based on spatially coupled low-density parity-check
(SC-LDPC) codes is presented. The proposed code ensembles are described by
protographs, comprised of several protograph-based chains characterizing
individual SC-LDPC codes. We demonstrate that code ensembles obtained by
connecting appropriately chosen SC-LDPC code chains at specific points have
improved iterative decoding thresholds compared to those of single SC-LDPC
coupled chains. In addition, it is shown that the improved decoding properties
of the connected ensembles result in reduced decoding complexity required to
achieve a specific bit error probability. The constructed ensembles are also
asymptotically good, in the sense that the minimum distance grows linearly with
the block length. Finally, we show that the improved asymptotic properties of
the connected chain ensembles also translate into improved finite length
performance.
|
1312.3379 | On RIC bounds of Compressed Sensing Matrices for Approximating Sparse
Solutions Using $\ell_q$ Quasi Norms | cs.IT math.IT math.OC | This paper follows the recent discussion on the sparse solution recovery with
quasi-norms $\ell_q,~q\in(0,1)$ when the sensing matrix possesses a Restricted
Isometry Constant $\delta_{2k}$ (RIC). Our key tool is an improvement on a
version of "the converse of a generalized Cauchy-Schwarz inequality" extended
to the setting of quasi-norm. We show that, if $\delta_{2k}\le 1/2$, any
minimizer of the $l_q$ minimization, at least for those $q\in(0,0.9181]$, is
the sparse solution of the corresponding underdetermined linear system.
Moreover, if $\delta_{2k}\le0.4931$, the sparse solution can be recovered by
any $l_q, q\in(0,1)$ minimization. The values $0.9181$ and $0.4931$ improves
those reported previously in the literature.
|
1312.3386 | Clustering for high-dimension, low-sample size data using distance
vectors | stat.ML cs.LG | In high-dimension, low-sample size (HDLSS) data, it is not always true that
closeness of two objects reflects a hidden cluster structure. We point out the
important fact that it is not the closeness, but the "values" of distance that
contain information of the cluster structure in high-dimensional space. Based
on this fact, we propose an efficient and simple clustering approach, called
distance vector clustering, for HDLSS data. Under the assumptions given in the
work of Hall et al. (2005), we show the proposed approach provides a true
cluster label under milder conditions when the dimension tends to infinity with
the sample size fixed. The effectiveness of the distance vector clustering
approach is illustrated through a numerical experiment and real data analysis.
|
1312.3387 | Navigating the massive world of reddit: Using backbone networks to map
user interests in social media | cs.SI physics.soc-ph | In the massive online worlds of social media, users frequently rely on
organizing themselves around specific topics of interest to find and engage
with like-minded people. However, navigating these massive worlds and finding
topics of specific interest often proves difficult because the worlds are
mostly organized haphazardly, leaving users to find relevant interests by word
of mouth or using a basic search feature. Here, we report on a method using the
backbone of a network to create a map of the primary topics of interest in any
social network. To demonstrate the method, we build an interest map for the
social news web site reddit and show how such a map could be used to navigate a
social media world. Moreover, we analyze the network properties of the reddit
social network and find that it has a scale-free, small-world, and modular
community structure, much like other online social networks such as Facebook
and Twitter. We suggest that the integration of interest maps into popular
social media platforms will assist users in organizing themselves into more
specific interest groups, which will help alleviate the overcrowding effect
often observed in large online communities.
|
1312.3388 | Online Bayesian Passive-Aggressive Learning | cs.LG | Online Passive-Aggressive (PA) learning is an effective framework for
performing max-margin online learning. But the deterministic formulation and
estimated single large-margin model could limit its capability in discovering
descriptive structures underlying complex data. This pa- per presents online
Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA
and extends naturally to incorporate latent variables and perform nonparametric
Bayesian inference, thus providing great flexibility for explorative analysis.
We apply BayesPA to topic modeling and derive efficient online learning
algorithms for max-margin topic models. We further develop nonparametric
methods to resolve the number of topics. Experimental results on real datasets
show that our approaches significantly improve time efficiency while
maintaining comparable results with the batch counterparts.
|
1312.3389 | Matrix Product Codes over Finite Commutative Frobenius Rings | cs.IT math.IT | Properties of matrix product codes over finite commutative Frobenius rings
are investigated. The minimum distance of matrix product codes constructed with
several types of matrices is bounded in different ways. The duals of matrix
product codes are also explicitly described in terms of matrix product codes.
|
1312.3393 | Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem | cs.LG | This paper proposes a new method for the K-armed dueling bandit problem, a
variation on the regular K-armed bandit problem that offers only relative
feedback about pairs of arms. Our approach extends the Upper Confidence Bound
algorithm to the relative setting by using estimates of the pairwise
probabilities to select a promising arm and applying Upper Confidence Bound
with the winner as a benchmark. We prove a finite-time regret bound of order
O(log t). In addition, our empirical results using real data from an
information retrieval application show that it greatly outperforms the state of
the art.
|
1312.3399 | Scalable Safety-Preserving Robust Control Synthesis for Continuous-Time
Linear Systems | cs.SY math.OC | We present a scalable set-valued safety-preserving controller for constrained
continuous-time linear time-invariant (LTI) systems subject to additive,
unknown but bounded disturbance or uncertainty. The approach relies upon a
conservative approximation of the discriminating kernel using robust maximal
reachable sets---an extension of our earlier work on computation of the
viability kernel for high-dimensional systems. Based on ellipsoidal techniques
for reachability, a piecewise ellipsoidal algorithm with polynomial complexity
is described that under-approximates the discriminating kernel under LTI
dynamics. This precomputed piecewise ellipsoidal set is then used online to
synthesize a permissive state-feedback safety-preserving controller. The
controller is modeled as a hybrid automaton and can be formulated such that
under certain conditions the resulting control signal is continuous across its
transitions. We show the performance of the controller on a twelve-dimensional
flight envelope protection problem for a quadrotor with actuation saturation
and unknown wind disturbances.
|
1312.3417 | Asymptotic MMSE Analysis Under Sparse Representation Modeling | cs.IT math.IT | Compressed sensing is a signal processing technique in which data is acquired
directly in a compressed form. There are two modeling approaches that can be
considered: the worst-case (Hamming) approach and a statistical mechanism, in
which the signals are modeled as random processes rather than as individual
sequences. In this paper, the second approach is studied. In particular, we
consider a model of the form $\boldsymbol{Y} =
\boldsymbol{H}\boldsymbol{X}+\boldsymbol{W}$, where each comportment of
$\boldsymbol{X}$ is given by $X_i = S_iU_i$, where $\left\{U_i\right\}$ are
i.i.d. Gaussian random variables, and $\left\{S_i\right\}$ are binary random
variables independent of $\left\{U_i\right\}$, and not necessarily independent
and identically distributed (i.i.d.), $\boldsymbol{H}\in\mathbb{R}^{k\times n}$
is a random matrix with i.i.d. entries, and $\boldsymbol{W}$ is white Gaussian
noise. Using a direct relationship between optimum estimation and certain
partition functions, and by invoking methods from statistical mechanics and
from random matrix theory (RMT), we derive an asymptotic formula for the
minimum mean-square error (MMSE) of estimating the input vector
$\boldsymbol{X}$ given $\boldsymbol{Y}$ and $\boldsymbol{H}$, as
$k,n\to\infty$, keeping the measurement rate, $R = k/n$, fixed. In contrast to
previous derivations, which are based on the replica method, the analysis
carried out in this paper is rigorous.
|
1312.3418 | One-Bit Compressed Sensing by Greedy Algorithms | cs.IT math.IT | Sign truncated matching pursuit (STrMP) algorithm is presented in this paper.
STrMP is a new greedy algorithm for the recovery of sparse signals from the
sign measurement, which combines the principle of consistent reconstruction
with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as
OMP and hence STrMP is simple to implement. In contrast to previous greedy
algorithms for one-bit compressed sensing, STrMP only need to solve a convex
and unconstraint subproblem at each iteration. Numerical experiments show that
STrMP is fast and accurate for one-bit compressed sensing compared with other
algorithms.
|
1312.3429 | Unsupervised learning of depth and motion | cs.CV cs.LG stat.ML | We present a model for the joint estimation of disparity and motion. The
model is based on learning about the interrelations between images from
multiple cameras, multiple frames in a video, or the combination of both. We
show that learning depth and motion cues, as well as their combinations, from
data is possible within a single type of architecture and a single type of
learning algorithm, by using biologically inspired "complex cell" like units,
which encode correlations between the pixels across image pairs. Our
experimental results show that the learning of depth and motion makes it
possible to achieve state-of-the-art performance in 3-D activity analysis, and
to outperform existing hand-engineered 3-D motion features by a very large
margin.
|
1312.3441 | Call Me MayBe: Understanding Nature and Risks of Sharing Mobile Numbers
on Online Social Networks | cs.SI cs.CY | There is a great concern about the potential for people to leak private
information on OSNs, but few quantitative studies on this. This research
explores the activity of sharing mobile numbers on OSNs, via public profiles
and posts. We attempt to understand the characteristics and risks of mobile
numbers sharing behaviour on OSNs and focus on Indian mobile numbers. We
collected 76,347 unique mobile numbers posted by 85905 users on Twitter and
Facebook and analysed 2997 numbers, prefixed with +91. We observed, most users
shared their own mobile numbers to spread urgent information; and to market
products and escort business. Fewer female users shared mobile numbers on OSNs.
Users utilized other OSN platforms and third party applications like
Twitterfeed, to post mobile numbers on multiple OSNs. In contrast to the user's
perception of numbers spreading quickly on OSN, we observed that except for
emergency, most numbers did not diffuse deep. To assess risks associated with
mobile numbers exposed on OSNs, we used numbers to gain sensitive information
about their owners (e.g. name, Voter ID) by collating publicly available data
from OSNs, Truecaller, OCEAN. On using the numbers on WhatApp, we obtained a
myriad of sensitive details (relationship status, BBM pins) of the number
owner. We communicated the observed risks to the owners by calling. Few users
were surprised to know about the online presence of their number, while a few
others intentionally posted it online for business purposes. We observed, 38.3%
of users who were unaware of the online presence of their number have posted
their number themselves on the social network. With these observations, we
highlight that there is a need to monitor leakage of mobile numbers via profile
and public posts. To the best of our knowledge, this is the first exploratory
study to critically investigate the exposure of Indian mobile numbers on OSNs.
|
1312.3496 | Memory effects induce structure in social networks with activity-driven
agents | physics.soc-ph cs.SI | Activity-driven modeling has been recently proposed as an alternative growth
mechanism for time varying networks, displaying power-law degree distribution
in time-aggregated representation. This approach assumes memoryless agents
developing random connections, thus leading to random networks that fail to
reproduce two-nodes degree correlations and the high clustering coefficient
widely observed in real social networks. In this work we introduce these
missing topological features by accounting for memory effects on the dynamic
evolution of time-aggregated networks. To this end, we propose an
activity-driven network growth model including a triadic-closure step as main
connectivity mechanism. We show that this mechanism provides some of the
fundamental topological features expected for social networks. We derive
analytical results and perform extensive numerical simulations in regimes with
and without population growth. Finally, we present two cases of study, one
comprising face-to-face encounters in a closed gathering, while the other one
from an online social friendship network.
|
1312.3507 | Transmission of a continuous signal via one-bit capacity channel | cs.IT math.IT math.OC | We study the problem of the transmission of currently observed time variable
signals via a channel that is capable of sending a single binary signal only
for each measurement of the underlying process. For encoding and decoding, we
suggest a modification othe adaptive delta modulation algorithm. This
modification ensures tracking of time variable signals. We obtained upper
estimates for the error for the case of noiseless transmission.
|
1312.3522 | Sparse Matrix-based Random Projection for Classification | cs.LG cs.CV stat.ML | As a typical dimensionality reduction technique, random projection can be
simply implemented with linear projection, while maintaining the pairwise
distances of high-dimensional data with high probability. Considering this
technique is mainly exploited for the task of classification, this paper is
developed to study the construction of random matrix from the viewpoint of
feature selection, rather than of traditional distance preservation. This
yields a somewhat surprising theoretical result, that is, the sparse random
matrix with exactly one nonzero element per column, can present better feature
selection performance than other more dense matrices, if the projection
dimension is sufficiently large (namely, not much smaller than the number of
feature elements); otherwise, it will perform comparably to others. For random
projection, this theoretical result implies considerable improvement on both
complexity and performance, which is widely confirmed with the classification
experiments on both synthetic data and real data.
|
1312.3532 | The utilization of social networking as promotion media (Case study:
Handicraft business in Palembang) | cs.SI cs.CY | Nowadays social media (Twitter, Facebook, etc.), not only simply as
communication media, but also for promotion. Social networking media offers
many business benefits for companies and organizations. Research purposes is to
determine the model of social network media utilization as a promotional media
for handicraft business in Palembang city. Qualitative and quantitative
research design are used to know how handicraft business in Palembang city
utilizing social media networking as a promotional media. The research results
show 35% craft businesses already utilizing social media as a promotional
media. The social media used are blog development 15%, facebook 46%, and
twitter etc. are 39%. The reasons they use social media such as, 1) minimal
cost, 2) easily recognizable, 3) global distribution areas. Social media
emphasis on direct engagement with customers better. So that the marketing
method could be more personal through direct communication with customers.
|
1312.3543 | Optimal Distributed Control for Networked Control Systems with Delays | cs.SY cs.GT | In networked control systems (NCS), sensing and control signals between the
plant and controllers are typically transmitted wirelessly. Thus, the time
delay plays an important role for the stability of NCS, especially with
distributed controllers. In this paper, the optimal control strategy is derived
for distributed control networks with time delays. In particular, we form the
optimal control problem as a non-cooperative linear quadratic game (LQG). Then,
the optimal control strategy of each controller is obtained that is based on
the current state and the last control strategies. The proposed optimal
distributed controller reduces to some known controllers under certain
conditions. Moreover, we illustrate the application of the proposed distributed
controller to load frequency control in power grid systems.
|
1312.3582 | Iterative Hard Thresholding for Weighted Sparse Approximation | cs.IT math.IT math.NA | Recent work by Rauhut and Ward developed a notion of weighted sparsity and a
corresponding notion of Restricted Isometry Property for the space of weighted
sparse signals. Using these notions, we pose a best weighted sparse
approximation problem, i.e. we seek structured sparse solutions to
underdetermined systems of linear equations. Many computationally efficient
greedy algorithms have been developed to solve the problem of best $s$-sparse
approximation. The design of all of these algorithms employ a similar template
of exploiting the RIP and computing projections onto the space of sparse
vectors. We present an extension of the Iterative Hard Thresholding (IHT)
algorithm to solve the weighted sparse approximation problem. This IHT
extension employs a weighted analogue of the template employed by all greedy
sparse approximation algorithms. Theoretical guarantees are presented and much
of the original analysis remains unchanged and extends quite naturally.
However, not all the theoretical analysis extends. To this end, we identify and
discuss the barrier to extension. Much like IHT, our IHT extension requires
computing a projection onto a non-convex space. However unlike IHT and other
greedy methods which deal with the classical notion of sparsity, no simple
method is known for computing projections onto these weighted sparse spaces.
Therefore we employ a surrogate for the projection and analyze its empirical
performance.
|
1312.3590 | Quantum computation and real multiplication | math-ph cs.IT math.IT math.MP | We propose a construction of anyon systems associated to quantum tori with
real multiplication and the embedding of quantum tori in AF algebras. These
systems generalize the Fibonacci anyons, with weaker categorical properties,
and are obtained from the basic modules and the real multiplication structure.
|
1312.3613 | Augur: a Modeling Language for Data-Parallel Probabilistic Inference | stat.ML cs.AI cs.DC cs.PL | It is time-consuming and error-prone to implement inference procedures for
each new probabilistic model. Probabilistic programming addresses this problem
by allowing a user to specify the model and having a compiler automatically
generate an inference procedure for it. For this approach to be practical, it
is important to generate inference code that has reasonable performance. In
this paper, we present a probabilistic programming language and compiler for
Bayesian networks designed to make effective use of data-parallel architectures
such as GPUs. Our language is fully integrated within the Scala programming
language and benefits from tools such as IDE support, type-checking, and code
completion. We show that the compiler can generate data-parallel inference code
scalable to thousands of GPU cores by making use of the conditional
independence relationships in the Bayesian network.
|
1312.3614 | Multiple Access Multicarrier Continuous-Variable Quantum Key
Distribution | quant-ph cs.IT math.IT | One of the most important practical realizations of the fundamentals of
quantum mechanics is continuous-variable quantum key distribution (CVQKD). Here
we propose the adaptive multicarrier quadrature division-multiuser quadrature
allocation (AMQD-MQA) multiple access technique for continuous-variable quantum
key distribution. The MQA scheme is based on the AMQD modulation, which
granulates the inputs of the users into Gaussian subcarrier
continuous-variables (CVs). In an AMQD-MQA multiple access scenario, the
simultaneous reliable transmission of the users is handled by the dynamic
allocation of the Gaussian subcarrier CVs. We propose two different settings of
AMQD-MQA for multiple input-multiple output communication. We introduce a
rate-selection strategy that tunes the modulation variances and allocates
adaptively the quadratures of the users over the sub-channels. We also prove
the rate formulas if only partial channel side information is available for the
users of the sub-channel conditions. We show a technique for the compensation
of a nonideal Gaussian input modulation, which allows the users to overwhelm
the modulation imperfections to reach optimal capacity-achieving communication
over the Gaussian sub-channels. We investigate the diversity amplification of
the sub-channel transmittance coefficients and reveal that a strong diversity
can be exploited by opportunistic Gaussian modulation.
|
1312.3631 | Distributed Function Computation Over a Rooted Directed Tree | cs.IT math.IT | This paper establishes the rate region for a class of source coding function
computation setups where sources of information are available at the nodes of a
tree and where a function of these sources must be computed at the root. The
rate region holds for any function as long as the sources' joint distribution
satisfies a certain Markov criterion. This criterion is met, in particular,
when the sources are independent.
This result recovers the rate regions of several function computation setups.
These include the point-to-point communication setting with arbitrary sources,
the noiseless multiple access network with "conditionally independent sources,"
and the cascade network with Markovian sources.
|
1312.3662 | Partially Overlapping Tones for Uncoordinated Networks | cs.IT math.IT | In an uncoordinated network, the link performance between the devices might
degrade significantly due to the interference from other links in the network
sharing the same spectrum. As a solution, in this study, the concept of
partially overlapping tones (POT) is introduced. The interference energy
observed at the victim receiver is mitigated by partially overlapping the
individual subcarriers via an intentional carrier frequency offset between the
links. Also, it is shown that while orthogonal transformations at the receiver
cannot mitigate the other-user interference without losing spectral efficiency,
non-orthogonal transformations are able to mitigate the other-user interference
without any spectral efficiency loss at the expense of self-interference. Using
spatial Poisson point process, a tractable bit error rate analysis is provided
to demonstrate potential benefits emerging from POT.
|
1312.3683 | Non-linear growth and condensation in multiplex networks | physics.soc-ph cond-mat.dis-nn cond-mat.stat-mech cs.SI | Different types of interactions coexist and coevolve to shape the structure
and function of a multiplex network. We propose here a general class of growth
models in which the various layers of a multiplex network coevolve through a
set of non-linear preferential attachment rules. We show, both numerically and
analytically, that by tuning the level of non-linearity these models allow to
reproduce either homogeneous or heterogeneous degree distributions, together
with positive or negative degree correlations across layers. In particular, we
derive the condition for the appearance of a condensed state in which one node
in each layer attracts an extensive fraction of all the edges.
|
1312.3693 | Policy Network Approach to Coordinated Disaster Response | cs.SI cs.CY physics.soc-ph | In this paper, we explore the formation of network relationships among
disaster relief agencies during the process of responding to an unexpected
event. The relationship is investigated through variables derived from the
policy network theory, and four cases from three developed countries such as
(i) Hurricane Katrina in the US; (ii) Typhoon Maemi in South Korea; (iii) Kobe;
and, (iv) Tohoku Earthquake in Japan that failed to cope with extreme events
forms the basis for case study presented here. We argue that structural
characteristics of multi-jurisdictional coordination may facilitate or impede
in responding to a complex nature of recent disaster. We further highlight the
promise of policy network approach in facilitating the development of
multi-jurisdictional coordination process which may provide new avenue to
improve the communication and coordination of hierarchical command control
driven organizations with the local community. Our proposed novel approach in
investigating the usefulness of network approach through media content analysis
for emergency may provide opportunity as a countermeasure to a traditional
hierarchical coordination, which may give further insights in establishing a
more effective network for emergency.
|
1312.3695 | Secure Beamforming for MIMO Two-Way Communications with an Untrusted
Relay | cs.IT math.IT | This paper studies the secure beamforming design in a multiple-antenna
three-node system where two source nodes exchange messages with the help of an
untrusted relay node. The relay acts as both an essential signal forwarder and
a potential eavesdropper. Both two-phase and three-phase two-way relay
strategies are considered. Our goal is to jointly optimize the source and relay
beamformers for maximizing the secrecy sum rate of the two-way communications.
We first derive the optimal relay beamformer structures. Then, iterative
algorithms are proposed to find source and relay beamformers jointly based on
alternating optimization. Furthermore, we conduct asymptotic analysis on the
maximum secrecy sum-rate. Our analysis shows that when all transmit powers
approach infinity, the two-phase two-way relay scheme achieves the maximum
secrecy sum rate if the source beamformers are designed such that the received
signals at the relay align in the same direction. This reveals an important
advantage of signal alignment technique in against eavesdropping. It is also
shown that if the source powers approach zero the three-phase scheme performs
the best while the two-phase scheme is even worse than direct transmission.
Simulation results have verified the efficiency of the secure beamforming
algorithms as well as the analytical findings.
|
1312.3702 | Outage Analysis of Uplink Two-tier Networks | cs.IT cs.NI math.IT | Employing multi-tier networks is among the most promising approaches to
address the rapid growth of the data demand in cellular networks. In this
paper, we study a two-tier uplink cellular network consisting of femtocells and
a macrocell. Femto base stations, and femto and macro users are assumed to be
spatially deployed based on independent Poisson point processes. We consider an
open access assignment policy, where each macro user based on the ratio between
its distances from its nearest femto access point (FAP) and from the macro base
station (MBS) is assigned to either of them. By tuning the threshold, this
policy allows controlling the coverage areas of FAPs. For a fixed threshold,
femtocells coverage areas depend on their distances from the MBS; Those closest
to the fringes will have the largest coverage areas. Under this open-access
policy, ignoring the additive noise, we derive analytical upper and lower
bounds on the outage probabilities of femto users and macro users that are
subject to fading and path loss. We also study the effect of the distance from
the MBS on the outage probability experienced by the users of a femtocell. In
all cases, our simulation results comply with our analytical bounds.
|
1312.3717 | Optimal algorithms for linear algebra by quantum inspiration | quant-ph cs.IT math.IT | Recent results by Harrow et. al. and by Ta-Shma, suggest that quantum
computers may have an exponential advantage in solving a wealth of linear
algebraic problems, over classical algorithms. Building on the quantum
intuition of these results, we step back into the classical domain, and explore
its usefulness in designing classical algorithms. We achieve an algorithm for
solving the major linear-algebraic problems in time $O(n^{\omega+\nu})$ for any
$\nu>0$, where $\omega$ is the optimal matrix-product constant. Thus our
algorithm is optimal w.r.t. matrix multiplication, and comparable to the
state-of-the-art algorithm for these problems due to Demmel et. al. Being
derived from quantum intuition, our proposed algorithm is completely disjoint
from all previous classical algorithms, and builds on a combination of
low-discrepancy sequences and perturbation analysis. As such, we hope it
motivates further exploration of quantum techniques in this respect, hopefully
leading to improvements in our understanding of space complexity and numerical
stability of these problems.
|
1312.3724 | ARIANNA: pAth Recognition for Indoor Assisted NavigatioN with Augmented
perception | cs.CV cs.HC | ARIANNA stands for pAth Recognition for Indoor Assisted Navigation with
Augmented perception. It is a flexible and low cost navigation system for vi-
sually impaired people. Arianna permits to navigate colored paths painted or
sticked on the floor revealing their directions through vibrational feedback on
commercial smartphones.
|
1312.3735 | Codes for Tasks and R\'enyi Entropy Rate | cs.IT math.IT | A task is randomly drawn from a finite set of tasks and is described using a
fixed number of bits. All the tasks that share its description must be
performed. Upper and lower bounds on the minimum $\rho$-th moment of the number
of performed tasks are derived. The key is an analog of the Kraft Inequality
for partitions of finite sets. When a sequence of tasks is produced by a source
of a given R\'enyi entropy rate of order $1/(1+\rho)$ and $n$ tasks are jointly
described using $nR$ bits, it is shown that for $R$ larger than the R\'enyi
entropy rate, the $\rho$-th moment of the ratio of performed tasks to $n$ can
be driven to one as $n$ tends to infinity, and that for $R$ less than the
R\'enyi entropy rate it tends to infinity. This generalizes a recent result for
IID sources by the same authors. A mismatched version of the direct part is
also considered, where the code is designed according to the wrong law. The
penalty incurred by the mismatch can be expressed in terms of a divergence
measure that was shown by Sundaresan to play a similar role in the
Massey-Arikan guessing problem.
|
1312.3738 | Path Based Mapping Technique for Robots | cs.RO | The purpose of this paper is to explore a new way of autonomous mapping.
Current systems using perception techniques like LAZER or SONAR use
probabilistic methods and have a drawback of allowing considerable uncertainty
in the mapping process. Our approach is to break down the environment,
specifically indoor, into reachable areas and objects, separated by boundaries,
and identifying their shape, to render various navigable paths around them.
This is a novel method to do away with uncertainties, as far as possible, at
the cost of temporal efficiency. Also this system demands only minimum and
cheap hardware, as it relies on only Infra-Red sensors to do the job.
|
1312.3748 | On Eavesdropper-Tolerance Capability of Two-Hop Wireless Networks | cs.IT math.IT | Two-hop wireless network serves as the basic net-work model for the study of
general wireless networks, while cooperative jamming is a promising scheme to
achieve the physi-cal layer security. This paper establishes a theoretical
framework for the study of eavesdropper-tolerance capability (i.e., the exact
maximum number of eavesdroppers that can be tolerated) in a two-hop wireless
network, where the cooperative jamming is adopted to ensure security defined by
secrecy outage probability (SOP) and opportunistic relaying is adopted to
guarantee relia-bility defined by transmission outage probability (TOP). For
the concerned network, closed form modeling for both SOP and TOP is first
conducted based on the Central Limit Theorem. With the help of SOP and TOP
models and also the Stochastic Ordering Theory, the model for
eavesdropper-tolerance capability analysis is then developed. Finally,
extensive simulation and numerical results are provided to illustrate the
efficiency of our theoretical framework as well as the eavesdropper-tolerance
capability of the concerned network from adopting cooperative jamming and
opportunistic relaying.
|
1312.3749 | Fibonacci Binning | cs.SI physics.soc-ph | This note argues that when dot-plotting distributions typically found in
papers about web and social networks (degree distributions, component-size
distributions, etc.), and more generally distributions that have high
variability in their tail, an exponentially binned version should always be
plotted, too, and suggests Fibonacci binning as a visually appealing,
easy-to-use and practical choice.
|
1312.3787 | Analysis and Understanding of Various Models for Efficient
Representation and Accurate Recognition of Human Faces | cs.CV | In this paper we have tried to compare the various face recognition models
against their classical problems. We look at the methods followed by these
approaches and evaluate to what extent they are able to solve the problems. All
methods proposed have some drawbacks under certain conditions. To overcome
these drawbacks we propose a multi-model approach
|
1312.3790 | Sample Complexity of Dictionary Learning and other Matrix Factorizations | stat.ML cs.IT math.IT | Many modern tools in machine learning and signal processing, such as sparse
dictionary learning, principal component analysis (PCA), non-negative matrix
factorization (NMF), $K$-means clustering, etc., rely on the factorization of a
matrix obtained by concatenating high-dimensional vectors from a training
collection. While the idealized task would be to optimize the expected quality
of the factors over the underlying distribution of training vectors, it is
achieved in practice by minimizing an empirical average over the considered
collection. The focus of this paper is to provide sample complexity estimates
to uniformly control how much the empirical average deviates from the expected
cost function. Standard arguments imply that the performance of the empirical
predictor also exhibit such guarantees. The level of genericity of the approach
encompasses several possible constraints on the factors (tensor product
structure, shift-invariance, sparsity \ldots), thus providing a unified
perspective on the sample complexity of several widely used matrix
factorization schemes. The derived generalization bounds behave proportional to
$\sqrt{\log(n)/n}$ w.r.t.\ the number of samples $n$ for the considered matrix
factorization techniques.
|
1312.3794 | Identification de r\^oles communautaires dans des r\'eseaux orient\'es
appliqu\'ee \`a Twitter | cs.SI | The notion of community structure is particularly useful when analyzing
complex networks, because it provides an intermediate level, compared to the
more classic global (whole network) and local (node neighborhood) approaches.
The concept of community role of a node was derived from this base, in order to
describe the position of a node in a network depending on its connectivity at
the community level. However, the existing approaches are restricted to
undirected networks, use topological measures which do not consider all aspects
of community-related connectivity, and their role identification methods are
not generalizable to all networks. We tackle these limitations by generalizing
and extending the measures, and using an unsupervised approach to determine the
roles. We then illustrate the applicability of our method by analyzing a
Twitter network.We show how our modifications allow discovering the fact some
particular users called social capitalists occupy very specific roles in this
system.
---
La notion de structure de communaut\'es est particuli\`erement utile pour
\'etudier les r\'eseaux complexes, car elle am\`ene un niveau d'analyse
interm\'ediaire, par opposition aux plus classiques niveaux local (voisinage
des noeuds) et global (r\'eseau entier). Le concept de r\^ole communautaire
permet de d\'ecrire le positionnement d'un noeud en fonction de sa
connectivit\'e communautaire. Cependant, les approches existantes sont
restreintes aux r\'eseaux non-orient\'es, utilisent des mesures topologiques ne
consid\'erant pas tous les aspects de la connectivit\'e communautaire, et des
m\'ethodes d'identification des r\^oles non-g\'en\'eralisables \`a tous les
r\'eseaux. Nous proposons de r\'esoudre ces probl\`emes en g\'en\'eralisant les
mesures existantes, et en utilisant une m\'ethode non-supervis\'ee pour
d\'eterminer les r\^oles. Nous illustrons l'int\'er\^et de notre m\'ethode en
l'appliquant au r\'eseau de Twitter. Nous montrons que nos modifications
mettent en \'evidence les r\^oles sp\'ecifiques d'utilisateurs particuliers du
r\'eseau, nomm\'es capitalistes sociaux.
|
1312.3808 | Information Maps: A Practical Approach to Position Dependent
Parameterization | cs.CE | In this contribution a practical approach to determine and store position
dependent parameters is presented. These parameters can be obtained, among
others, using experimental results or expert knowledge and are stored in
'Information Maps'. Each Information Map can be interpreted as a kind of static
grid map and the framework allows to link different maps hierarchically. The
Information Maps can be local or global, with static and dynamic information in
it. One application of Information Maps is the representation of position
dependent characteristics of a sensor. Thus, for instance, it is feasible to
store arbitrary attributes of a sensor's preprocessing in an Information Map
and utilize them by simply taking the map value at the current position. This
procedure is much more efficient than using the attributes of the sensor
itself. Some examples where and how Information Maps can be used are presented
in this publication. The Information Map is meant to be a simple and practical
approach to the problem of position dependent parameterization in all kind of
algorithms when the analytical description is not possible or can not be
implemented efficiently.
|
1312.3811 | Efficient Baseline-free Sampling in Parameter Exploring Policy
Gradients: Super Symmetric PGPE | cs.LG | Policy Gradient methods that explore directly in parameter space are among
the most effective and robust direct policy search methods and have drawn a lot
of attention lately. The basic method from this field, Policy Gradients with
Parameter-based Exploration, uses two samples that are symmetric around the
current hypothesis to circumvent misleading reward in \emph{asymmetrical}
reward distributed problems gathered with the usual baseline approach. The
exploration parameters are still updated by a baseline approach - leaving the
exploration prone to asymmetric reward distributions. In this paper we will
show how the exploration parameters can be sampled quasi symmetric despite
having limited instead of free parameters for exploration. We give a
transformation approximation to get quasi symmetric samples with respect to the
exploration without changing the overall sampling distribution. Finally we will
demonstrate that sampling symmetrically also for the exploration parameters is
superior in needs of samples and robustness than the original sampling
approach.
|
1312.3822 | Quantum Achievability Proof via Collision Relative Entropy | quant-ph cs.IT math.IT | In this paper, we provide a simple framework for deriving one-shot achievable
bounds for some problems in quantum information theory. Our framework is based
on the joint convexity of the exponential of the collision relative entropy,
and is a (partial) quantum generalization of the technique of Yassaee et al.
(2013) from classical information theory. Based on this framework, we derive
one-shot achievable bounds for the problems of communication over
classical-quantum channels, quantum hypothesis testing, and classical data
compression with quantum side information. We argue that our one-shot
achievable bounds are strong enough to give the asymptotic achievable rates of
these problems even up to the second order.
|
1312.3823 | Network error correction with limited feedback capacity | cs.IT math.IT | We consider the problem of characterizing network capacity in the presence of
adversarial errors on network links,focusing in particular on the effect of
low-capacity feedback links cross network cuts.
|
1312.3825 | Parkinson's Disease Motor Symptoms in Machine Learning: A Review | cs.AI | This paper reviews related work and state-of-the-art publications for
recognizing motor symptoms of Parkinson's Disease (PD). It presents research
efforts that were undertaken to inform on how well traditional machine learning
algorithms can handle this task. In particular, four PD related motor symptoms
are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia)
and their details summarized. Thus the primary objective of this research is to
provide a literary foundation for development and improvement of algorithms for
detecting PD related motor symptoms.
|
1312.3837 | Tables of parameters of symmetric configurations $v_{k}$ | math.CO cs.IT math.IT | Tables of the currently known parameters of symmetric configurations are
given. Formulas for parameters of the known infinite families of symmetric
configurations are presented as well. The results of the recent paper [18] are
used. This work can be viewed as an appendix to [18], in the sense that the
tables given here cover a much larger set of parameters.
|
1312.3838 | Invited review: Epidemics on social networks | nlin.AO cs.SI physics.soc-ph q-bio.PE | Since its first formulations almost a century ago, mathematical models for
disease spreading contributed to understand, evaluate and control the epidemic
processes.They promoted a dramatic change in how epidemiologists thought of the
propagation of infectious diseases.In the last decade, when the traditional
epidemiological models seemed to be exhausted, new types of models were
developed.These new models incorporated concepts from graph theory to describe
and model the underlying social structure.Many of these works merely produced a
more detailed extension of the previous results, but some others triggered a
completely new paradigm in the mathematical study of epidemic processes. In
this review, we will introduce the basic concepts of epidemiology, epidemic
modeling and networks, to finally provide a brief description of the most
relevant results in the field.
|
1312.3858 | Computational impact of hydrophobicity in protein stability | cs.CE | Among the various features of amino acids, the hydrophobic property has most
visible impact on stability of a sequence folding. This is mentioned in many
protein folding related work, in this paper we more elaborately discuss the
computational impact of the well defined hydrophobic aspect in determining
stability, approach with the help of a developed free energy computing
algorithm covering various aspects preprocessing of an amino acid sequence,
generating the folding and calculating free energy. Later discussing its use in
protein structure related research work.
|
1312.3872 | Eugene Garfield, Francis Narin, and PageRank: The Theoretical Bases of
the Google Search Engine | cs.IR cs.DL physics.soc-ph | This paper presents a test of the validity of using Google Scholar to
evaluate the publications of researchers by comparing the premises on which its
search engine, PageRank, is based, to those of Garfield's theory of citation
indexing. It finds that the premises are identical and that PageRank and
Garfield's theory of citation indexing validate each other.
|
1312.3876 | The Symmetric Convex Ordering: A Novel Partial Order for B-DMCs Ordering
the Information Sets of Polar Codes | cs.IT math.IT | In this paper, we propose a novel partial order for binary discrete
memoryless channels that we call the symmetric convex ordering. We show that
Ar{\i}kan's polar transform preserves 'symmetric convex orders'. Furthermore,
we show that while for symmetric channels this ordering turns out to be
equivalent to the stochastic degradation ordering already known to order the
information sets of polar codes, a strictly weaker partial order is obtained
when at least one of the channels is asymmetric. In between, we also discuss
two tools which can be useful for verifying this ordering: a criterion known as
the cut criterion and channel symmetrization. Finally, we discuss potential
applications of the results to polar coding over non-stationary channels.
|
1312.3889 | Cyclotomy of Weil Sums of Binomials | math.NT cs.IT math.CO math.IT | The Weil sum $W_{K,d}(a)=\sum_{x \in K} \psi(x^d + a x)$ where $K$ is a
finite field, $\psi$ is an additive character of $K$, $d$ is coprime to
$|K^\times|$, and $a \in K^\times$ arises often in number-theoretic
calculations, and in applications to finite geometry, cryptography, digital
sequence design, and coding theory. Researchers are especially interested in
the case where $W_{K,d}(a)$ assumes three distinct values as $a$ runs through
$K^\times$. A Galois-theoretic approach, combined with $p$-divisibility results
on Gauss sums, is used here to prove a variety of new results that constrain
which fields $K$ and exponents $d$ support three-valued Weil sums, and restrict
the values that such Weil sums may assume.
|
1312.3903 | A Methodology for Player Modeling based on Machine Learning | cs.AI cs.LG | AI is gradually receiving more attention as a fundamental feature to increase
the immersion in digital games. Among the several AI approaches, player
modeling is becoming an important one. The main idea is to understand and model
the player characteristics and behaviors in order to develop a better AI. In
this work, we discuss several aspects of this new field. We proposed a taxonomy
to organize the area, discussing several facets of this topic, ranging from
implementation decisions up to what a model attempts to describe. We then
classify, in our taxonomy, some of the most important works in this field. We
also presented a generic approach to deal with player modeling using ML, and we
instantiated this approach to model players' preferences in the game
Civilization IV. The instantiation of this approach has several steps. We first
discuss a generic representation, regardless of what is being modeled, and
evaluate it performing experiments with the strategy game Civilization IV.
Continuing the instantiation of the proposed approach we evaluated the
applicability of using game score information to distinguish different
preferences. We presented a characterization of virtual agents in the game,
comparing their behavior with their stated preferences. Once we have
characterized these agents, we were able to observe that different preferences
generate different behaviors, measured by several game indicators. We then
tackled the preference modeling problem as a binary classification task, with a
supervised learning approach. We compared four different methods, based on
different paradigms (SVM, AdaBoost, NaiveBayes and JRip), evaluating them on a
set of matches played by different virtual agents. We conclude our work using
the learned models to infer human players' preferences. Using some of the
evaluated classifiers we obtained accuracies over 60% for most of the inferred
preferences.
|
1312.3913 | Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies | cs.DB | Privacy definitions provide ways for trading-off the privacy of individuals
in a statistical database for the utility of downstream analysis of the data.
In this paper, we present Blowfish, a class of privacy definitions inspired by
the Pufferfish framework, that provides a rich interface for this trade-off. In
particular, we allow data publishers to extend differential privacy using a
policy, which specifies (a) secrets, or information that must be kept secret,
and (b) constraints that may be known about the data. While the secret
specification allows increased utility by lessening protection for certain
individual properties, the constraint specification provides added protection
against an adversary who knows correlations in the data (arising from
constraints). We formalize policies and present novel algorithms that can
handle general specifications of sensitive information and certain count
constraints. We show that there are reasonable policies under which our privacy
mechanisms for k-means clustering, histograms and range queries introduce
significantly lesser noise than their differentially private counterparts. We
quantify the privacy-utility trade-offs for various policies analytically and
empirically on real datasets.
|
1312.3961 | Fundamental Limits of Caching with Secure Delivery | cs.IT cs.NI math.IT | Caching is emerging as a vital tool for alleviating the severe capacity
crunch in modern content-centric wireless networks. The main idea behind
caching is to store parts of popular content in end-users' memory and leverage
the locally stored content to reduce peak data rates. By jointly designing
content placement and delivery mechanisms, recent works have shown order-wise
reduction in transmission rates in contrast to traditional methods. In this
work, we consider the secure caching problem with the additional goal of
minimizing information leakage to an external wiretapper. The fundamental cache
memory vs. transmission rate trade-off for the secure caching problem is
characterized. Rather surprisingly, these results show that security can be
introduced at a negligible cost, particularly for large number of files and
users. It is also shown that the rate achieved by the proposed caching scheme
with secure delivery is within a constant multiplicative factor from the
information-theoretic optimal rate for almost all parameter values of practical
interest.
|
1312.3968 | Generalized Approximate Message Passing for Cosparse Analysis
Compressive Sensing | cs.IT math.IT | In cosparse analysis compressive sensing (CS), one seeks to estimate a
non-sparse signal vector from noisy sub-Nyquist linear measurements by
exploiting the knowledge that a given linear transform of the signal is
cosparse, i.e., has sufficiently many zeros. We propose a novel approach to
cosparse analysis CS based on the generalized approximate message passing
(GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works
with a wide range of analysis operators and regularizers. In addition, we
propose a novel $\ell_0$-like soft-thresholder based on MMSE denoising for a
spike-and-slab distribution with an infinite-variance slab. Numerical
demonstrations on synthetic and practical datasets demonstrate advantages over
existing AMP-based, greedy, and reweighted-$\ell_1$ approaches.
|
1312.3970 | An Extensive Evaluation of Filtering Misclassified Instances in
Supervised Classification Tasks | cs.LG stat.ML | Removing or filtering outliers and mislabeled instances prior to training a
learning algorithm has been shown to increase classification accuracy. A
popular approach for handling outliers and mislabeled instances is to remove
any instance that is misclassified by a learning algorithm. However, an
examination of which learning algorithms to use for filtering as well as their
effects on multiple learning algorithms over a large set of data sets has not
been done. Previous work has generally been limited due to the large
computational requirements to run such an experiment, and, thus, the
examination has generally been limited to learning algorithms that are
computationally inexpensive and using a small number of data sets. In this
paper, we examine 9 learning algorithms as filtering algorithms as well as
examining the effects of filtering in the 9 chosen learning algorithms on a set
of 54 data sets. In addition to using each learning algorithm individually as a
filter, we also use the set of learning algorithms as an ensemble filter and
use an adaptive algorithm that selects a subset of the learning algorithms for
filtering for a specific task and learning algorithm. We find that for most
cases, using an ensemble of learning algorithms for filtering produces the
greatest increase in classification accuracy. We also compare filtering with a
majority voting ensemble. The voting ensemble significantly outperforms
filtering unless there are high amounts of noise present in the data set.
Additionally, we find that a majority voting ensemble is robust to noise as
filtering with a voting ensemble does not increase the classification accuracy
of the voting ensemble.
|
1312.3971 | Balancing bike sharing systems (BBSS): instance generation from the
CitiBike NYC data | cs.AI | Bike sharing systems are a very popular means to provide bikes to citizens in
a simple and cheap way. The idea is to install bike stations at various points
in the city, from which a registered user can easily loan a bike by removing it
from a specialized rack. After the ride, the user may return the bike at any
station (if there is a free rack). Services of this kind are mainly public or
semi-public, often aimed at increasing the attractiveness of non-motorized
means of transportation, and are usually free, or almost free, of charge for
the users. Depending on their location, bike stations have specific patterns
regarding when they are empty or full. For instance, in cities where most jobs
are located near the city centre, the commuters cause certain peaks in the
morning: the central bike stations are filled, while the stations in the
outskirts are emptied. Furthermore, stations located on top of a hill are more
likely to be empty, since users are less keen on cycling uphill to return the
bike, and often leave their bike at a more reachable station. These issues
result in substantial user dissatisfaction which may eventually cause the users
to abandon the service. This is why nowadays most bike sharing system providers
take measures to rebalance them. Over the last few years, balancing bike
sharing systems (BBSS) has become increasingly studied in optimization. As
such, generating meaningful instance to serve as a benchmark for the proposed
approaches is an important task. In this technical report we describe the
procedure we used to generate BBSS problem instances from data of the CitiBike
NYC bike sharing system.
|
1312.3981 | Joint multi-mode dispersion extraction in Fourier and space time domains | physics.geo-ph cs.IT math.IT | In this paper we present a novel broadband approach for the extraction of
dispersion curves of multiple time frequency overlapped dispersive modes such
as in borehole acoustic data. The new approach works jointly in the Fourier and
space time domains and, in contrast to existing space time approaches that
mainly work for time frequency separated signals, efficiently handles multiple
signals with significant time frequency overlap. The proposed method begins by
exploiting the slowness (phase and group) and time location estimates based on
frequency-wavenumber (f-k) domain sparsity penalized broadband dispersion
extraction method as presented in \cite{AeronTSP2011}. In this context we first
present a Cramer Rao Bound (CRB) analysis for slowness estimation in the (f-k)
domain and show that for the f-k domain broadband processing, group slowness
estimates have more variance than the phase slowness estimates and time
location estimates. In order to improve the group slowness estimates we exploit
the time compactness property of the modes to effectively represent the data as
a linear superposition of time compact space time propagators parameterized by
the phase and group slowness. A linear least squares estimation algorithm in
the space time domain is then used to obtain improved group slowness estimates.
The performance of the method is demonstrated on real borehole acoustic data
sets.
|
1312.3986 | Correlations between user voting data, budget, and box office for films
in the Internet Movie Database | physics.soc-ph cs.SI | The Internet Movie Database (IMDb) is one of the most-visited websites in the
world and the premier source for information on films. Like Wikipedia, much of
IMDb's information is user contributed. IMDb also allows users to voice their
opinion on the quality of films through voting. We investigate whether there is
a connection between this user voting data and certain economic film
characteristics. To this end, we perform distribution and correlation analysis
on a set of films chosen to mitigate effects of bias due to the language and
country of origin of films. We show that production budget, box office gross,
and total number of user votes for films are consistent with double-log normal
distributions for certain time periods. Both total gross and user votes are
consistent with a double-log normal distribution from the late 1980s onward,
while for budget, it extends from 1935 to 1979. In addition, we find a strong
correlation between number of user votes and the economic statistics,
particularly budget. Remarkably, we find no evidence for a correlation between
number of votes and average user rating. As previous studies have found a
strong correlation between production budget and marketing expenses, our
results suggest that total user votes is an indicator of a film's prominence or
notability, which can be quantified by its promotional costs.
|
1312.3989 | Classifiers With a Reject Option for Early Time-Series Classification | cs.CV cs.LG | Early classification of time-series data in a dynamic environment is a
challenging problem of great importance in signal processing. This paper
proposes a classifier architecture with a reject option capable of online
decision making without the need to wait for the entire time series signal to
be present. The main idea is to classify an odor/gas signal with an acceptable
accuracy as early as possible. Instead of using posterior probability of a
classifier, the proposed method uses the "agreement" of an ensemble to decide
whether to accept or reject the candidate label. The introduced algorithm is
applied to the bio-chemistry problem of odor classification to build a novel
Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel
test-bed facility confirms the robustness of the forefront-nose compared to the
standard classifiers from both earliness and recognition perspectives.
|
1312.3990 | ECOC-Based Training of Neural Networks for Face Recognition | cs.CV cs.LG | Error Correcting Output Codes, ECOC, is an output representation method
capable of discovering some of the errors produced in classification tasks.
This paper describes the application of ECOC to the training of feed forward
neural networks, FFNN, for improving the overall accuracy of classification
systems. Indeed, to improve the generalization of FFNN classifiers, this paper
proposes an ECOC-Based training method for Neural Networks that use ECOC as the
output representation, and adopts the traditional Back-Propagation algorithm,
BP, to adjust weights of the network. Experimental results for face recognition
problem on Yale database demonstrate the effectiveness of our method. With a
rejection scheme defined by a simple robustness rate, high reliability is
achieved in this application.
|
1312.4003 | Asynchronous Physical-Layer Network Coding with Quasi-Cyclic Codes | cs.IT math.IT | Communication in the presence of bounded timing asynchronism which is known
to the receiver but cannot be easily compensated is studied. Examples of such
situations include point-to-point communication over inter-symbol interference
(ISI) channels and asynchronous wireless networks. In these scenarios, although
the receiver may know all the delays, it is often not be an easy task for the
receiver to compensate the delays as the signals are mixed together. A novel
framework called interleave/deinterleave transform (IDT) is proposed to deal
with this problem. It is shown that the IDT allows one to design the delays so
that quasi-cyclic (QC) codes with a proper shifting constraint can be used
accordingly. When used in conjunction with QC codes, IDT provides significantly
better performance than existing schemes relying solely on cyclic codes. Two
instances of asynchronous physical-layer network coding, namely the
integer-forcing equalization for ISI channels and asynchronous
compute-and-forward, are then studied. For integer-forcing equalization, the
proposed scheme provides improved performance over using cyclic codes. For
asynchronous compute-and-forward, the proposed scheme shows that there is no
loss in the achievable information due to delays which are integer multiples of
the symbol duration. Further, the proposed approach shows that delays
introduced by the channel can sometimes be exploited to obtain higher
information rates than those obtainable in the synchronous case. The proposed
IDT can be thought of as a generalization of the interleaving/deinterleaving
idea proposed by Wang et al. which allows the use of QC codes thereby
substantially increasing the design space.
|
1312.4012 | Oblivious Query Processing | cs.DB | Motivated by cloud security concerns, there is an increasing interest in
database systems that can store and support queries over encrypted data. A
common architecture for such systems is to use a trusted component such as a
cryptographic co-processor for query processing that is used to securely
decrypt data and perform computations in plaintext. The trusted component has
limited memory, so most of the (input and intermediate) data is kept encrypted
in an untrusted storage and moved to the trusted component on ``demand.''
In this setting, even with strong encryption, the data access pattern from
untrusted storage has the potential to reveal sensitive information; indeed,
all existing systems that use a trusted component for query processing over
encrypted data have this vulnerability. In this paper, we undertake the first
formal study of secure query processing, where an adversary having full
knowledge of the query (text) and observing the query execution learns nothing
about the underlying database other than the result size of the query on the
database. We introduce a simpler notion, oblivious query processing, and show
formally that a query admits secure query processing iff it admits oblivious
query processing. We present oblivious query processing algorithms for a rich
class of database queries involving selections, joins, grouping and
aggregation. For queries not handled by our algorithms, we provide some initial
evidence that designing oblivious (and therefore secure) algorithms would be
hard via reductions from two simple, well-studied problems that are generally
believed to be hard. Our study of oblivious query processing also reveals
interesting connections to database join theory.
|
1312.4026 | Achieving Fully Proportional Representation: Approximability Results | cs.AI cs.GT cs.MA | We study the complexity of (approximate) winner determination under the
Monroe and Chamberlin--Courant multiwinner voting rules, which determine the
set of representatives by optimizing the total (dis)satisfaction of the voters
with their representatives. The total (dis)satisfaction is calculated either as
the sum of individual (dis)satisfactions (the utilitarian case) or as the
(dis)satisfaction of the worst off voter (the egalitarian case). We provide
good approximation algorithms for the satisfaction-based utilitarian versions
of the Monroe and Chamberlin--Courant rules, and inapproximability results for
the dissatisfaction-based utilitarian versions of them and also for all
egalitarian cases. Our algorithms are applicable and particularly appealing
when voters submit truncated ballots. We provide experimental evaluation of the
algorithms both on real-life preference-aggregation data and on synthetic data.
These experiments show that our simple and fast algorithms can in many cases
find near-perfect solutions.
|
1312.4036 | Mind Your Language: Effects of Spoken Query Formulation on Retrieval
Effectiveness | cs.IR | Voice search is becoming a popular mode for interacting with search engines.
As a result, research has gone into building better voice transcription
engines, interfaces, and search engines that better handle inherent verbosity
of queries. However, when one considers its use by non- native speakers of
English, another aspect that becomes important is the formulation of the query
by users. In this paper, we present the results of a preliminary study that we
conducted with non-native English speakers who formulate queries for given
retrieval tasks. Our results show that the current search engines are sensitive
in their rankings to the query formulation, and thus highlights the need for
developing more robust ranking methods.
|
1312.4044 | CACO : Competitive Ant Colony Optimization, A Nature-Inspired
Metaheuristic For Large-Scale Global Optimization | cs.NE | Large-scale problems are nonlinear problems that need metaheuristics, or
global optimization algorithms. This paper reviews nature-inspired
metaheuristics, then it introduces a framework named Competitive Ant Colony
Optimization inspired by the chemical communications among insects. Then a case
study is presented to investigate the proposed framework for large-scale global
optimization.
|
1312.4048 | Toward an agent based distillation approach for protesting crowd
simulation | cs.MA | This paper investigates the problem of protesting crowd simulation. It
considers CROCADILE, an agent based distillation system, for this purpose. A
model of protesting crowd was determined and then a CROCADILE model of
protesting crowd was engineered and demonstrated. We validated the model by
using two scenarios where protesters are varied with different personalities.
The results indicated that CROCADILE served well as the platform for protesting
crowd modeling simulation
|
1312.4074 | Clustering using Vector Membership: An Extension of the Fuzzy C-Means
Algorithm | cs.CV | Clustering is an important facet of explorative data mining and finds
extensive use in several fields. In this paper, we propose an extension of the
classical Fuzzy C-Means clustering algorithm. The proposed algorithm,
abbreviated as VFC, adopts a multi-dimensional membership vector for each data
point instead of the traditional, scalar membership value defined in the
original algorithm. The membership vector for each point is obtained by
considering each feature of that point separately and obtaining individual
membership values for the same. We also propose an algorithm to efficiently
allocate the initial cluster centers close to the actual centers, so as to
facilitate rapid convergence. Further, we propose a scheme to achieve crisp
clustering using the VFC algorithm. The proposed, novel clustering scheme has
been tested on two standard data sets in order to analyze its performance. We
also examine the efficacy of the proposed scheme by analyzing its performance
on image segmentation examples and comparing it with the classical Fuzzy
C-means clustering algorithm.
|
1312.4076 | Elementos de ingenier\'ia de explotaci\'on de la informaci\'on:
r\'eplica y algunos trazos sobre teor\'ia inform\'atica | cs.IT math.IT | A reply to the commentaries of Yana (2013), and some jots on information
theory.
|
1312.4078 | A natural-inspired optimization machine based on the annual migration of
salmons in nature | cs.NE | Bio inspiration is a branch of artificial simulation science that shows
pervasive contributions to variety of engineering fields such as automate
pattern recognition, systematic fault detection and applied optimization. In
this paper, a new metaheuristic optimizing algorithm that is the simulation of
The Great Salmon Run(TGSR) is developed. The obtained results imply on the
acceptable performance of implemented method in optimization of complex non
convex, multi dimensional and multi-modal problems. To prove the superiority of
TGSR in both robustness and quality, it is also compared with most of the well
known proposed optimizing techniques such as Simulated Annealing (SA), Parallel
Migrating Genetic Algorithm (PMGA), Differential Evolutionary Algorithm (DEA),
Particle Swarm Optimization (PSO), Bee Algorithm (BA), Artificial Bee Colony
(ABC), Firefly Algorithm (FA) and Cuckoo Search (CS). The obtained results
confirm the acceptable performance of the proposed method in both robustness
and quality for different bench-mark optimizing problems and also prove the
authors claim.
|
1312.4091 | On Dissemination Time of Random Linear Network Coding in Ad-hoc Networks | cs.IT math.IT | Random linear network coding (RLNC) unicast protocol is analyzed over a
rapidly-changing network topology. We model the probability mass function (pmf)
of the dissemination time as a sequence of independent geometric random
variables whose success probability changes with every successful reception of
an innovative packet. We derive a tight approximation of the average networked
innovation probability conditioned on network dimension increase. We show
through simulations that our approximations for the average dissemination time
and its pmf are tight. We then propose to use a RLNC-based broadcast
dissemination protocol over a general dynamic topology where nodes are chosen
for transmission based on average innovative information that they can provided
to the rest of the network. Simulation results show that information
disseminates considerably faster as opposed to standard RLNC algorithm where
nodes are chosen uniformly at random.
|
1312.4092 | Domain adaptation for sequence labeling using hidden Markov models | cs.CL cs.LG | Most natural language processing systems based on machine learning are not
robust to domain shift. For example, a state-of-the-art syntactic dependency
parser trained on Wall Street Journal sentences has an absolute drop in
performance of more than ten points when tested on textual data from the Web.
An efficient solution to make these methods more robust to domain shift is to
first learn a word representation using large amounts of unlabeled data from
both domains, and then use this representation as features in a supervised
learning algorithm. In this paper, we propose to use hidden Markov models to
learn word representations for part-of-speech tagging. In particular, we study
the influence of using data from the source, the target or both domains to
learn the representation and the different ways to represent words using an
HMM.
|
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