id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1403.3780 | Automatic Classification of Human Epithelial Type 2 Cell Indirect
Immunofluorescence Images using Cell Pyramid Matching | q-bio.CB cs.CV q-bio.QM | This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.
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1403.3785 | Statistically validated mobile communication networks: Evolution of
motifs in European and Chinese data | physics.soc-ph cs.SI | Big data open up unprecedented opportunities to investigate complex systems
including the society. In particular, communication data serve as major sources
for computational social sciences but they have to be cleaned and filtered as
they may contain spurious information due to recording errors as well as
interactions, like commercial and marketing activities, not directly related to
the social network. The network constructed from communication data can only be
considered as a proxy for the network of social relationships. Here we apply a
systematic method, based on multiple hypothesis testing, to statistically
validate the links and then construct the corresponding Bonferroni network,
generalized to the directed case. We study two large datasets of mobile phone
records, one from Europe and the other from China. For both datasets we compare
the raw data networks with the corresponding Bonferroni networks and point out
significant differences in the structures and in the basic network measures. We
show evidence that the Bonferroni network provides a better proxy for the
network of social interactions than the original one. By using the filtered
networks we investigated the statistics and temporal evolution of small
directed 3-motifs and conclude that closed communication triads have a
formation time-scale, which is quite fast and typically intraday. We also find
that open communication triads preferentially evolve to other open triads with
a higher fraction of reciprocated calls. These stylized facts were observed for
both datasets.
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1403.3786 | Universal Decoding for Gaussian Intersymbol Interference Channels | cs.IT math.IT | A universal decoding procedure is proposed for the intersymbol interference
(ISI) Gaussian channels. The universality of the proposed decoder is in the
sense of being independent of the various channel parameters, and at the same
time, attaining the same random coding error exponent as the optimal
maximum-likelihood (ML) decoder, which utilizes full knowledge of these unknown
parameters. The proposed decoding rule can be regarded as a frequency domain
version of the universal maximum mutual information (MMI) decoder. Contrary to
previously suggested universal decoders for ISI channels, our proposed decoding
metric can easily be evaluated.
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1403.3788 | MIMO Zero-Forcing Performance Evaluation Using the Holonomic Gradient
Method | cs.IT math.IT | For multiple-input multiple-output (MIMO) spatial-multiplexing transmission,
zero-forcing detection (ZF) is appealing because of its low complexity. Our
recent MIMO ZF performance analysis for Rician--Rayleigh fading, which is
relevant in heterogeneous networks, has yielded for the ZF outage probability
and ergodic capacity infinite-series expressions. Because they arose from
expanding the confluent hypergeometric function $ {_1\! F_1} (\cdot, \cdot,
\sigma) $ around 0, they do not converge numerically at realistically-high
Rician $ K $-factor values. Therefore, herein, we seek to take advantage of the
fact that $ {_1\! F_1} (\cdot, \cdot, \sigma) $ satisfies a differential
equation, i.e., it is a \textit{holonomic} function. Holonomic functions can be
computed by the \textit{holonomic gradient method} (HGM), i.e., by numerically
solving the satisfied differential equation. Thus, we first reveal that the
moment generating function (m.g.f.) and probability density function (p.d.f.)
of the ZF signal-to-noise ratio (SNR) are holonomic. Then, from the
differential equation for $ {_1\! F_1} (\cdot, \cdot, \sigma) $, we deduce
those satisfied by the SNR m.g.f. and p.d.f., and demonstrate that the HGM
helps compute the p.d.f. accurately at practically-relevant values of $ K $.
Finally, numerical integration of the SNR p.d.f. produced by HGM yields
accurate ZF outage probability and ergodic capacity results.
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1403.3795 | Think Locally, Act Locally: The Detection of Small, Medium-Sized, and
Large Communities in Large Networks | cs.SI cond-mat.dis-nn math.CO nlin.AO physics.soc-ph | It is common in the study of networks to investigate meso-scale features to
try to gain an understanding of network structure and function. For example,
numerous algorithms have been developed to try to identify "communities," which
are typically construed as sets of nodes with denser connections internally
than with the remainder of a network. In this paper, we adopt a complementary
perspective that "communities" are associated with bottlenecks of
locally-biased dynamical processes that begin at seed sets of nodes, and we
employ several different community-identification procedures (using
diffusion-based and geodesic-based dynamics) to investigate community quality
as a function of community size. Using several empirical and synthetic
networks, we identify several distinct scenarios for ``size-resolved community
structure'' that can arise in real (and realistic) networks. Depending on which
scenario holds, one may or may not be able to successfully identify ``good''
communities in a given network, the manner in which different small communities
fit together to form meso-scale network structures can be very different, and
processes such as viral propagation and information diffusion can exhibit very
different dynamics.In addition, our results suggest that, for many large
realistic networks, the output of locally-biased methods that focus on
communities that are centered around a given seed node might have better
conceptual grounding and greater practical utility than the output of global
community-detection methods. They also illustrate subtler structural properties
that are important to consider in the development of better benchmark networks
to test methods for community detection.
[Note: Because of space limitations in the arXiv's abstract field, this is an
abridged version of the paper's abstract.]
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1403.3807 | Sensing Subjective Well-being from Social Media | cs.AI cs.CY | Subjective Well-being(SWB), which refers to how people experience the quality
of their lives, is of great use to public policy-makers as well as economic,
sociological research, etc. Traditionally, the measurement of SWB relies on
time-consuming and costly self-report questionnaires. Nowadays, people are
motivated to share their experiences and feelings on social media, so we
propose to sense SWB from the vast user generated data on social media. By
utilizing 1785 users' social media data with SWB labels, we train machine
learning models that are able to "sense" individual SWB from users' social
media. Our model, which attains the state-by-art prediction accuracy, can then
be used to identify SWB of large population of social media users in time with
very low cost.
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1403.3829 | Geometric VLAD for Large Scale Image Search | cs.CV | We present a novel compact image descriptor for large scale image search. Our
proposed descriptor - Geometric VLAD (gVLAD) is an extension of VLAD (Vector of
Locally Aggregated Descriptors) that incorporates weak geometry information
into the VLAD framework. The proposed geometry cues are derived as a membership
function over keypoint angles which contain evident and informative information
but yet often discarded. A principled technique for learning the membership
function by clustering angles is also presented. Further, to address the
overhead of iterative codebook training over real-time datasets, a novel
codebook adaptation strategy is outlined. Finally, we demonstrate the efficacy
of proposed gVLAD based retrieval framework where we achieve more than 15%
improvement in mAP over existing benchmarks.
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1403.3864 | Information Anatomy of Stochastic Equilibria | cond-mat.stat-mech cs.IT math.DS math.IT nlin.CD | A stochastic nonlinear dynamical system generates information, as measured by
its entropy rate. Some---the ephemeral information---is dissipated and
some---the bound information---is actively stored and so affects future
behavior. We derive analytic expressions for the ephemeral and bound
informations in the limit of small-time discretization for two classical
systems that exhibit dynamical equilibria: first-order Langevin equations (i)
where the drift is the gradient of a potential function and the diffusion
matrix is invertible and (ii) with a linear drift term (Ornstein-Uhlenbeck) but
a noninvertible diffusion matrix. In both cases, the bound information is
sensitive only to the drift, while the ephemeral information is sensitive only
to the diffusion matrix and not to the drift. Notably, this information anatomy
changes discontinuously as any of the diffusion coefficients vanishes,
indicating that it is very sensitive to the noise structure. We then calculate
the information anatomy of the stochastic cusp catastrophe and of particles
diffusing in a heat bath in the overdamped limit, both examples of stochastic
gradient descent on a potential landscape. Finally, we use our methods to
calculate and compare approximations for the so-called time-local predictive
information for adaptive agents.
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1403.3881 | Complexity of Equilibrium in Diffusion Games on Social Networks | cs.GT cs.CC cs.DC cs.DM cs.MA | In this paper, we consider the competitive diffusion game, and study the
existence of its pure-strategy Nash equilibrium when defined over general
undirected networks. We first determine the set of pure-strategy Nash
equilibria for two special but well-known classes of networks, namely the
lattice and the hypercube. Characterizing the utility of the players in terms
of graphical distances of their initial seed placements to other nodes in the
network, we show that in general networks the decision process on the existence
of pure-strategy Nash equilibrium is an NP-hard problem. Following this, we
provide some necessary conditions for a given profile to be a Nash equilibrium.
Furthermore, we study players' utilities in the competitive diffusion game over
Erdos-Renyi random graphs and show that as the size of the network grows, the
utilities of the players are highly concentrated around their expectation, and
are bounded below by some threshold based on the parameters of the network.
Finally, we obtain a lower bound for the maximum social welfare of the game
with two players, and study sub-modularity of the players' utilities.
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1403.3891 | Exploiting Regional Differences: A Spatially Adaptive Random Access | cs.IT cs.NI math.IT | In this paper, we discuss the potential for improvement of the simple random
access scheme by utilizing local information such as the received
signal-to-interference-plus-noise-ratio (SINR). We propose a spatially adaptive
random access (SARA) scheme in which the transmitters in the network utilize
different transmit probabilities depending on the local situation. In our
proposed scheme, the transmit probability is adaptively updated by the ratio of
the received SINR and the target SINR. We investigate the performance of the
spatially adaptive random access scheme. For the comparison, we derive an
optimal transmit probability of ALOHA random access scheme in which all
transmitters use the same transmit probability. We illustrate the performance
of the spatially adaptive random access scheme through simulations. We show
that the performance of the proposed scheme surpasses that of the optimal ALOHA
random access scheme and is comparable with the CSMA/CA scheme.
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1403.3909 | Graph Sample and Hold: A Framework for Big-Graph Analytics | cs.SI cs.DB physics.soc-ph stat.AP | Sampling is a standard approach in big-graph analytics; the goal is to
efficiently estimate the graph properties by consulting a sample of the whole
population. A perfect sample is assumed to mirror every property of the whole
population. Unfortunately, such a perfect sample is hard to collect in complex
populations such as graphs (e.g. web graphs, social networks etc), where an
underlying network connects the units of the population. Therefore, a good
sample will be representative in the sense that graph properties of interest
can be estimated with a known degree of accuracy. While previous work focused
particularly on sampling schemes used to estimate certain graph properties
(e.g. triangle count), much less is known for the case when we need to estimate
various graph properties with the same sampling scheme. In this paper, we
propose a generic stream sampling framework for big-graph analytics, called
Graph Sample and Hold (gSH). To begin, the proposed framework samples from
massive graphs sequentially in a single pass, one edge at a time, while
maintaining a small state. We then show how to produce unbiased estimators for
various graph properties from the sample. Given that the graph analysis
algorithms will run on a sample instead of the whole population, the runtime
complexity of these algorithm is kept under control. Moreover, given that the
estimators of graph properties are unbiased, the approximation error is kept
under control. Finally, we show the performance of the proposed framework (gSH)
on various types of graphs, such as social graphs, among others.
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1403.3931 | Quickest detection in coupled systems | math.OC cs.IT math.IT math.ST stat.TH | This work considers the problem of quickest detection of signals in a coupled
system of $N$ sensors, which receive continuous sequential observations from
the environment. It is assumed that the signals, which are modeled by general
It\^{o} processes, are coupled across sensors, but that their onset times may
differ from sensor to sensor. Two main cases are considered; in the first one
signal strengths are the same across sensors while in the second one they
differ by a constant. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended minimal
Kullback-Leibler divergence criterion is used as a measure of detection delay,
with a constraint on the mean time to the first false alarm. The case in which
the sensors employ cumulative sum (CUSUM) strategies is considered, and it is
proved that the minimum of $N$ CUSUMs is asymptotically optimal as the mean
time to the first false alarm increases without bound. In particular, in the
case of equal signal strengths across sensors, it is seen that the difference
in detection delay of the $N$-CUSUM stopping rule and the unknown optimal
stopping scheme tends to a constant related to the number of sensors as the
mean time to the first false alarm increases without bound. Alternatively, in
the case of unequal signal strengths, it is seen that this difference tends to
zero.
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1403.3948 | An Improved Apriori Algorithm for Association Rules | cs.DB | There are several mining algorithms of association rules. One of the most
popular algorithms is Apriori that is used to extract frequent itemsets from
large database and getting the association rule for discovering the knowledge.
Based on this algorithm, this paper indicates the limitation of the original
Apriori algorithm of wasting time for scanning the whole database searching on
the frequent itemsets, and presents an improvement on Apriori by reducing that
wasted time depending on scanning only some transactions. The paper shows by
experimental results with several groups of transactions, and with several
values of minimum support that applied on the original Apriori and our
implemented improved Apriori that our improved Apriori reduces the time
consumed by 67.38% in comparison with the original Apriori, and makes the
Apriori algorithm more efficient and less time consuming.
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1403.3964 | Image processing using miniKanren | cs.CV cs.PL | An integral image is one of the most efficient optimization technique for
image processing. However an integral image is only a special case of delayed
stream or memoization. This research discusses generalizing concept of integral
image optimization technique, and how to generate an integral image optimized
program code automatically from abstracted image processing algorithm. In oder
to abstruct algorithms, we forces to miniKanren.
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1403.3978 | A Novel Scheme for Downlink Opportunistic Interference Alignment | cs.IT math.IT | In this paper we propose a downlink codebook-based opportunistic interference
alignment (OIA) in a three-cell MIMO system. A codebook composed of multiple
transmit vector sets is utilized to improve the multiuser selection diversity.
The sum rate increases as the size of the codebook grows. In addition, during
the user selection, effective channel gain and alignment metric are combined to
generate a novel criterion, which improves the system performance, especially
at low SNR. Furthermore, a threshold-based feedback approach is introduced to
reduce the feedback load in the proposed scheme. Both the analytical results
and simulations show that the proposed scheme provides significant improvement
in terms of sum rates with no feedback load growth and slight increase of
complexity.
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1403.3991 | Throughput Optimization for Massive MIMO Systems Powered by Wireless
Energy Transfer | cs.IT math.IT | This paper studies a wireless-energy-transfer (WET) enabled massive
multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid
data-and-energy access point (H-AP) and multiple single-antenna users. In the
WET-MM system, the H-AP is equipped with a large number $M$ of antennas and
functions like a conventional AP in receiving data from users, but additionally
supplies wireless power to the users. We consider frame-based transmissions.
Each frame is divided into three phases: the uplink channel estimation (CE)
phase, the downlink WET phase, as well as the uplink wireless information
transmission (WIT) phase. Firstly, users use a fraction of the previously
harvested energy to send pilots, while the H-AP estimates the uplink channels
and obtains the downlink channels by exploiting channel reciprocity. Next, the
H-AP utilizes the channel estimates just obtained to transfer wireless energy
to all users in the downlink via energy beamforming. Finally, the users use a
portion of the harvested energy to send data to the H-AP simultaneously in the
uplink (reserving some harvested energy for sending pilots in the next frame).
To optimize the throughput and ensure rate fairness, we consider the problem of
maximizing the minimum rate among all users. In the large-$M$ regime, we obtain
the asymptotically optimal solutions and some interesting insights for the
optimal design of WET-MM system. We define a metric, namely, the massive MIMO
degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by
$\log(M)$. We show that the proposed WET-MM system is optimal in terms of
MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.
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1403.4011 | Whose Opinion to follow in Multihypothesis Social Learning? A Large
Deviations Perspective | cs.IT math.IT | We consider a multihypothesis social learning problem in which an agent has
access to a set of private observations and chooses an opinion from a set of
experts to incorporate into its final decision. To model individual biases, we
allow the agent and experts to have general loss functions and possibly
different decision spaces. We characterize the loss exponents of both the agent
and experts, and provide an asymptotically optimal method for the agent to
choose the best expert to follow. We show that up to asymptotic equivalence,
the worst loss exponent for the agent is achieved when it adopts the 0-1 loss
function, which assigns a loss of 0 if the true hypothesis is declared and a
loss of 1 otherwise. We introduce the concept of hypothesis-loss neutrality,
and show that if the agent adopts a particular policy that is hypothesis-loss
neutral, then it ignores all experts whose decision spaces are smaller than its
own. On the other hand, if experts have the same decision space as the agent,
then choosing an expert with the same loss function as itself is not
necessarily optimal for the agent, which is somewhat counter-intuitive. We
derive sufficient conditions for when it is optimal for the agent with 0-1 loss
function to choose an expert with the same loss function.
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1403.4015 | Exceptional planar polynomials | math.NT cs.IT math.CO math.IT | Planar functions are special functions from a finite field to itself that
give rise to finite projective planes and other combinatorial objects. We
consider polynomials over a finite field $K$ that induce planar functions on
infinitely many extensions of $K$; we call such polynomials exceptional planar.
Exceptional planar monomials have been recently classified. In this paper we
establish a partial classification of exceptional planar polynomials. This
includes results for the classical planar functions on finite fields of odd
characteristic and for the recently proposed planar functions on finite fields
of characteristic two.
|
1403.4017 | Multi-task Feature Selection based Anomaly Detection | stat.ML cs.LG | Network anomaly detection is still a vibrant research area. As the fast
growth of network bandwidth and the tremendous traffic on the network, there
arises an extremely challengeable question: How to efficiently and accurately
detect the anomaly on multiple traffic? In multi-task learning, the traffic
consisting of flows at different time periods is considered as a task. Multiple
tasks at different time periods performed simultaneously to detect anomalies.
In this paper, we apply the multi-task feature selection in network anomaly
detection area which provides a powerful method to gather information from
multiple traffic and detect anomalies on it simultaneously. In particular, the
multi-task feature selection includes the well-known l1-norm based feature
selection as a special case given only one task. Moreover, we show that the
multi-task feature selection is more accurate by utilizing more information
simultaneously than the l1-norm based method. At the evaluation stage, we
preprocess the raw data trace from trans-Pacific backbone link between Japan
and the United States, label with anomaly communities, and generate a
248-feature dataset. We show empirically that the multi-task feature selection
outperforms independent l1-norm based feature selection on real traffic
dataset.
|
1403.4023 | Simulation leagues: Analysis of competition formats | cs.MA cs.AI cs.RO | The selection of an appropriate competition format is critical for both the
success and credibility of any competition, both real and simulated. In this
paper, the automated parallelism offered by the RoboCupSoccer 2D simulation
league is leveraged to conduct a 28,000 game round-robin between the top 8
teams from RoboCup 2012 and 2013. A proposed new competition format is found to
reduce variation from the resultant statistically significant team performance
rankings by 75% and 67%, when compared to the actual competition results from
RoboCup 2012 and 2013 respectively. These results are statistically validated
by generating 10,000 random tournaments for each of the three considered
formats and comparing the respective distributions of ranking discrepancy.
|
1403.4024 | Measuring Global Similarity between Texts | cs.CL | We propose a new similarity measure between texts which, contrary to the
current state-of-the-art approaches, takes a global view of the texts to be
compared. We have implemented a tool to compute our textual distance and
conducted experiments on several corpuses of texts. The experiments show that
our methods can reliably identify different global types of texts.
|
1403.4047 | A Queueing Characterization of Information Transmission over Block
Fading Rayleigh Channels in the Low SNR | cs.IT math.IT | Unlike the AWGN (additive white gaussian noise) channel, fading channels
suffer from random channel gains besides the additive Gaussian noise. As a
result, the instantaneous channel capacity varies randomly along time, which
makes it insufficient to characterize the transmission capability of a fading
channel using data rate only. In this paper, the transmission capability of a
buffer-aided block Rayleigh fading channel is examined by a constant rate input
data stream, and reflected by several parameters such as the average queue
length, stationary queue length distribution, packet delay and overflow
probability. Both infinite-buffer model and finite-buffer model are considered.
Taking advantage of the memoryless property of the service provided by the
channel in each block in the the low SNR (signal-to-noise ratio) regime, the
information transmission over the channel is formulated as a \textit{discrete
time discrete state} $D/G/1$ queueing problem. The obtained results show that
block fading channels are unable to support a data rate close to their ergodic
capacity, no matter how long the buffer is, even seen from the application
layer. For the finite-buffer model, the overflow probability is derived with
explicit expression, and is shown to decrease exponentially when buffer size is
increased, even when the buffer size is very small.
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1403.4060 | Quantum codes from affine variety codes and their subfield-subcodes | cs.IT math.IT | We use affine variety codes and their subfield-subcodes for obtaining quantum
stabilizer codes via the CSS code construction. With this procedure, we get
codes with good parameters and a code whose parameters exceed the CSS quantum
Gilbert-Varshamov bound given by Feng and Ma.
|
1403.4099 | High-speed detection of emergent market clustering via an unsupervised
parallel genetic algorithm | q-fin.CP cs.DC cs.NE | We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.
|
1403.4106 | The role of endogenous and exogenous mechanisms in the formation of R&D
networks | physics.soc-ph cs.SI physics.data-an | We develop an agent-based model of strategic link formation in Research and
Development (R&D) networks. Empirical evidence has shown that the growth of
these networks is driven by mechanisms which are both endogenous to the system
(that is, depending on existing alliances patterns) and exogenous (that is,
driven by an exploratory search for newcomer firms). Extant research to date
has not investigated both mechanisms simultaneously in a comparative manner. To
overcome this limitation, we develop a general modeling framework to shed light
on the relative importance of these two mechanisms. We test our model against a
comprehensive dataset, listing cross-country and cross-sectoral R&D alliances
from 1984 to 2009. Our results show that by fitting only three macroscopic
properties of the network topology, this framework is able to reproduce a
number of micro-level measures, including the distributions of degree, local
clustering, path length and component size, and the emergence of network
clusters. Furthermore, by estimating the link probabilities towards newcomers
and established firms from the data, we find that endogenous mechanisms are
predominant over the exogenous ones in the network formation, thus quantifying
the importance of existing structures in selecting partner firms.
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1403.4109 | Convergence Time for Unbiased Quantized Consensus Over Static and
Dynamic Networks | cs.SY cs.SI | In this paper, the question of expected time to convergence is addressed for
unbiased quantized consensus on undirected connected graphs, and some strong
results are obtained. The paper first provides a tight expression for the
expected convergence time of the unbiased quantized consensus over general but
fixed networks. It is shown that the maximum expected convergence time lies
within a constant factor of the maximum hitting time of an appropriate lazy
random walk, using the theory of harmonic functions for reversible Markov
chains. Following this, and using electric resistance analogy of the reversible
Markov chains, the paper provides a tight upper bound for the expected
convergence time to consensus based on the parameters of the network. Moreover,
the paper identifies a precise order of the maximum expected convergence time
for some simple graphs such as line graph and cycle. Finally, the results are
extended to bound the expected convergence time of the underlying dynamics in
time-varying networks. Modeling such dynamics as the evolution of a time
inhomogeneous Markov chain, the paper derives a tight upper bound for expected
convergence time of the dynamics using the spectral representation of the
networks. This upper bound is significantly better than earlier results for the
quantized consensus problem over time-varying graphs.
|
1403.4118 | Efficient Maximum-Likelihood Decoding of Linear Block Codes on Binary
Memoryless Channels | cs.IT math.IT | In this work, we consider efficient maximum-likelihood decoding of linear
block codes for small-to-moderate block lengths. The presented approach is a
branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel
(IEEE Trans. Inf. Theory, 2012) for obtaining lower bounds. We have compared
our proposed algorithm to the state-of-the-art commercial integer program
solver CPLEX, and for all considered codes our approach is faster for both low
and high signal-to-noise ratios. For instance, for the benchmark (155,64)
Tanner code our algorithm is more than 11 times as fast as CPLEX for an SNR of
1.0 dB on the additive white Gaussian noise channel. By a small modification,
our algorithm can be used to calculate the minimum distance, which we have
again verified to be much faster than using the CPLEX solver.
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1403.4155 | Bayesian Design of Tandem Networks for Distributed Detection With
Multi-bit Sensor Decisions | cs.IT math.IT | We consider the problem of decentralized hypothesis testing under
communication constraints in a topology where several peripheral nodes are
arranged in tandem. Each node receives an observation and transmits a message
to its successor, and the last node then decides which hypothesis is true. We
assume that the observations at different nodes are, conditioned on the true
hypothesis, independent and the channel between any two successive nodes is
considered error-free but rate-constrained. We propose a cyclic numerical
design algorithm for the design of nodes using a person-by-person methodology
with the minimum expected error probability as a design criterion, where the
number of communicated messages is not necessarily equal to the number of
hypotheses. The number of peripheral nodes in the proposed method is in
principle arbitrary and the information rate constraints are satisfied by
quantizing the input of each node. The performance of the proposed method for
different information rate constraints, in a binary hypothesis test, is
compared to the optimum rate-one solution due to Swaszek and a method proposed
by Cover, and it is shown numerically that increasing the channel rate can
significantly enhance the performance of the tandem network. Simulation results
for $M$-ary hypothesis tests also show that by increasing the channel rates the
performance of the tandem network significantly improves.
|
1403.4174 | A Receding Horizon Approach to Multi-Agent Planning from Local LTL
Specifications | cs.RO | We study the problem of control synthesis for multi-agent systems, to achieve
complex, high-level, long-term goals that are assigned to each agent
individually. As the agents might not be capable of satisfying their respective
goals by themselves, requests for other agents' collaborations are a part of
the task descriptions. Particularly, we consider that the task specification
takes a form of a linear temporal logic formula, which may contain requirements
and constraints on the other agent's behavior. A traditional automata-based
approach to multi-agent strategy synthesis from such specifications builds on
centralized planning for the whole team and thus suffers from extreme
computational demands. In this work, we aim at reducing the computational
complexity by decomposing the strategy synthesis problem into short horizon
planning problems that are solved iteratively, upon the run of the agents. We
discuss the correctness of the solution and find assumptions, under which the
proposed iterative algorithm leads to provable eventual satisfaction of the
desired specifications.
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1403.4175 | Approximate Dynamic Programming based on Projection onto the (min,+)
subsemimodule | cs.SY math.OC | We develop a new Approximate Dynamic Programming (ADP) method for infinite
horizon discounted reward Markov Decision Processes (MDP) based on projection
onto a subsemimodule. We approximate the value function in terms of a
$(\min,+)$ linear combination of a set of basis functions whose $(\min,+)$
linear span constitutes a subsemimodule. The projection operator is closely
related to the Fenchel transform. Our approximate solution obeys the $(\min,+)$
Projected Bellman Equation (MPPBE) which is different from the conventional
Projected Bellman Equation (PBE). We show that the approximation error is
bounded in its $L_\infty$-norm. We develop a Min-Plus Approximate Dynamic
Programming (MPADP) algorithm to compute the solution to the MPPBE. We also
present the proof of convergence of the MPADP algorithm and apply it to two
problems, a grid-world problem in the discrete domain and mountain car in the
continuous domain.
|
1403.4179 | Approximate dynamic programming with $(\min,+)$ linear function
approximation for Markov decision processes | cs.SY math.OC | Markov Decision Processes (MDP) is an useful framework to cast optimal
sequential decision making problems. Given any MDP the aim is to find the
optimal action selection mechanism i.e., the optimal policy. Typically, the
optimal policy ($u^*$) is obtained by substituting the optimal value-function
($J^*$) in the Bellman equation. Alternately $u^*$ is also obtained by learning
the optimal state-action value function $Q^*$ known as the $Q$ value-function.
However, it is difficult to compute the exact values of $J^*$ or $Q^*$ for MDPs
with large number of states. Approximate Dynamic Programming (ADP) methods
address this difficulty by computing lower dimensional approximations of
$J^*$/$Q^*$. Most ADP methods employ linear function approximation (LFA), i.e.,
the approximate solution lies in a subspace spanned by a family of pre-selected
basis functions. The approximation is obtain via a linear least squares
projection of higher dimensional quantities and the $L_2$ norm plays an
important role in convergence and error analysis. In this paper, we discuss ADP
methods for MDPs based on LFAs in $(\min,+)$ algebra. Here the approximate
solution is a $(\min,+)$ linear combination of a set of basis functions whose
span constitutes a subsemimodule. Approximation is obtained via a projection
operator onto the subsemimodule which is different from linear least squares
projection used in ADP methods based on conventional LFAs. MDPs are not
$(\min,+)$ linear systems, nevertheless, we show that the monotonicity property
of the projection operator helps us to establish the convergence of our ADP
schemes. We also discuss future directions in ADP methods for MDPs based on the
$(\min,+)$ LFAs.
|
1403.4182 | Effects of Spatial Randomness on Locating a Point Source with
Distributed Sensors | cs.IT math.IT | Most studies that consider the problem of estimating the location of a point
source in wireless sensor networks assume that the source location is estimated
by a set of spatially distributed sensors, whose locations are fixed. Motivated
by the fact that the observation quality and performance of the localization
algorithm depend on the location of the sensors, which could be randomly
distributed, this paper investigates the performance of a recently proposed
energy-based source-localization algorithm under the assumption that the
sensors are positioned according to a uniform clustering process. Practical
considerations such as the existence and size of the exclusion zones around
each sensor and the source will be studied. By introducing a novel performance
measure called the estimation outage, it will be shown how parameters related
to the network geometry such as the distance between the source and the closest
sensor to it as well as the number of sensors within a region surrounding the
source affect the localization performance.
|
1403.4224 | Learning Negative Mixture Models by Tensor Decompositions | cs.LG | This work considers the problem of estimating the parameters of negative
mixture models, i.e. mixture models that possibly involve negative weights. The
contributions of this paper are as follows. (i) We show that every rational
probability distributions on strings, a representation which occurs naturally
in spectral learning, can be computed by a negative mixture of at most two
probabilistic automata (or HMMs). (ii) We propose a method to estimate the
parameters of negative mixture models having a specific tensor structure in
their low order observable moments. Building upon a recent paper on tensor
decompositions for learning latent variable models, we extend this work to the
broader setting of tensors having a symmetric decomposition with positive and
negative weights. We introduce a generalization of the tensor power method for
complex valued tensors, and establish theoretical convergence guarantees. (iii)
We show how our approach applies to negative Gaussian mixture models, for which
we provide some experiments.
|
1403.4232 | Automatic Image Registration in Infrared-Visible Videos using Polygon
Vertices | cs.CV | In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method.
|
1403.4238 | Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU
Co-Processing | cs.DC cs.CV cs.MS | In this paper, we present an OpenCL-based heterogeneous implementation of a
computer vision algorithm -- image inpainting-based object removal algorithm --
on mobile devices. To take advantage of the computation power of the mobile
processor, the algorithm workflow is partitioned between the CPU and the GPU
based on the profiling results on mobile devices, so that the
computationally-intensive kernels are accelerated by the mobile GPGPU
(general-purpose computing using graphics processing units). By exploring the
implementation trade-offs and utilizing the proposed optimization strategies at
different levels including algorithm optimization, parallelism optimization,
and memory access optimization, we significantly speed up the algorithm with
the CPU-GPU heterogeneous implementation, while preserving the quality of the
output images. Experimental results show that heterogeneous computing based on
GPGPU co-processing can significantly speed up the computer vision algorithms
and makes them practical on real-world mobile devices.
|
1403.4267 | Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit | cs.NA cs.LG | We investigate the methods that simultaneously enforce sparsity and low-rank
structure in a matrix as often employed for sparse phase retrieval problems or
phase calibration problems in compressive sensing. We propose a new approach
for analyzing the trade off between the sparsity and low rank constraints in
these approaches which not only helps to provide guidelines to adjust the
weights between the aforementioned constraints, but also enables new simulation
strategies for evaluating performance. We then provide simulation results for
phase retrieval and phase calibration cases both to demonstrate the consistency
of the proposed method with other approaches and to evaluate the change of
performance with different weights for the sparsity and low rank structure
constraints.
|
1403.4289 | Telling Breaking News Stories from Wikipedia with Social Multimedia: A
Case Study of the 2014 Winter Olympics | cs.SI cs.CY cs.IR | With the ability to watch Wikipedia and Wikidata edits in realtime, the
online encyclopedia and the knowledge base have become increasingly used
targets of research for the detection of breaking news events. In this paper,
we present a case study of the 2014 Winter Olympics, where we tell the story of
breaking news events in the context of the Olympics with the help of social
multimedia stemming from multiple social network sites. Therefore, we have
extended the application Wikipedia Live Monitor-a tool for the detection of
breaking news events-with the capability of automatically creating media
galleries that illustrate events. Athletes winning an Olympic competition, a
new country leading the medal table, or simply the Olympics themselves are all
events newsworthy enough for people to concurrently edit Wikipedia and
Wikidata-around the world in many languages. The Olympics being an event of
common interest, an even bigger majority of people share the event in a
multitude of languages on global social network sites, which makes the event an
ideal subject of study. With this work, we connect the world of Wikipedia and
Wikidata with the world of social network sites, in order to convey the spirit
of the 2014 Winter Olympics, to tell the story of victory and defeat, and
always following the Olympic motto Citius, Altius, Fortius. The proposed
system-generalized for all sort of breaking news stories-has been put in
production in form of the Twitter bot @mediagalleries, available and archived
at https://twitter.com/mediagalleries.
|
1403.4303 | A new network node similarity measure method and its applications | physics.soc-ph cs.SI | Network node similarity measure has been paid particular attention in the
field of statistical physics. In this paper, we utilize the concept of
information and information loss to measure the node similarity. The whole
model is based on this idea that if two nodes are more similar than the others,
then the information loss of seeing them as the same is less. The present new
method has low algorithm complexity so that it can save much time and energy to
deal with the large scale real-world network. We illustrate the availability of
this approach based on two artificial examples and computer-generated networks
by comparing its accuracy with the other selected approaches. The above tests
demonstrate that the new method can provide more reasonable results consistent
with our human common judgment. The new similarity measure method is also
applied to predict the network evolution and predict unknown nodes'
attributions in the two application examples.
|
1403.4311 | The lower bound of the PCM quantization error in high dimension | math.NA cs.IT math.IT | In this note, we investigate the performance of the PCM scheme with linear
quantization rule for quantizing unit-norm tight frame expansions for ${\mathbb
R}^d$ without the White Noise Hypothesis. In \cite{WX}, Wang and Xu showed that
for asymptotically equidistributed unit-norm tight frame the PCM quantization
error has an upper bound ${\mathcal O}(\delta^{(d+1)/2})$ and they conjecture
the upper bound is sharp. In this note, we confirm the conjecture with
employing the asymptotic estimate of the Bessel functions.
|
1403.4333 | Increasing Flash Memory Lifetime by Dynamic Voltage Allocation for
Constant Mutual Information | cs.IT math.IT | The read channel in Flash memory systems degrades over time because the
Fowler-Nordheim tunneling used to apply charge to the floating gate eventually
compromises the integrity of the cell because of tunnel oxide degradation.
While degradation is commonly measured in the number of program/erase cycles
experienced by a cell, the degradation is proportional to the number of
electrons forced into the floating gate and later released by the erasing
process. By managing the amount of charge written to the floating gate to
maintain a constant read-channel mutual information, Flash lifetime can be
extended. This paper proposes an overall system approach based on information
theory to extend the lifetime of a flash memory device. Using the instantaneous
storage capacity of a noisy flash memory channel, our approach allocates the
read voltage of flash cell dynamically as it wears out gradually over time. A
practical estimation of the instantaneous capacity is also proposed based on
soft information via multiple reads of the memory cells.
|
1403.4334 | Bregman Divergences for Infinite Dimensional Covariance Matrices | cs.CV | We introduce an approach to computing and comparing Covariance Descriptors
(CovDs) in infinite-dimensional spaces. CovDs have become increasingly popular
to address classification problems in computer vision. While CovDs offer some
robustness to measurement variations, they also throw away part of the
information contained in the original data by only retaining the second-order
statistics over the measurements. Here, we propose to overcome this limitation
by first mapping the original data to a high-dimensional Hilbert space, and
only then compute the CovDs. We show that several Bregman divergences can be
computed between the resulting CovDs in Hilbert space via the use of kernels.
We then exploit these divergences for classification purposes. Our experiments
demonstrate the benefits of our approach on several tasks, such as material and
texture recognition, person re-identification, and action recognition from
motion capture data.
|
1403.4342 | Spatial Performance Analysis and Design Principles for Wireless Peer
Discovery | cs.IT cs.NI math.IT | In wireless peer-to-peer networks that serve various proximity-based
applications, peer discovery is the key to identifying other peers with which a
peer can communicate and an understanding of its performance is fundamental to
the design of an efficient discovery operation. This paper analyzes the
performance of wireless peer discovery through comprehensively considering the
wireless channel, spatial distribution of peers, and discovery operation
parameters. The average numbers of successfully discovered peers are expressed
in closed forms for two widely used channel models, i.e., the interference
limited Nakagami-m fading model and the Rayleigh fading model with nonzero
noise, when peers are spatially distributed according to a homogeneous Poisson
point process. These insightful expressions lead to the design principles for
the key operation parameters including the transmission probability, required
amount of wireless resources, level of modulation and coding scheme (MCS), and
transmit power. Furthermore, the impact of shadowing on the spatial performance
and suggested design principles is evaluated using mathematical analysis and
simulations.
|
1403.4346 | Spatial Topology Adjustment for Minimizing Multicell Network Power
Consumption | cs.IT math.IT | While the deployment of base stations (BSs) becomes increasingly dense in
order to accommodate the growth in traffic demand, these BSs may be
under-utilized during most hours except peak hours. Accordingly, the
deactivation of these under-utilized BSs is regarded as the key to reducing
network power consumption; however, the remaining active BSs should increase
their transmit power in order to fill network coverage holes that result from
BS switching off. This paper investigates the optimal balance between such
beneficial and harmful effects of BS switching off in terms of minimizing the
network power consumption, through comprehensively considering the spatial BS
distribution, BS transmit power, BS power consumption behaviors, radio
propagation environments, and frequency reuse. When BSs are deployed according
to a homogeneous Poisson point process, the suboptimal and approximated design
problems are formulated as geometric programming and the solutions lead to
insightful design principles for the key design parameters including the
spatial density, transmit power, and frequency reuse of remaining active BSs.
The numerical results demonstrate that these solutions are very close to the
optimal balances.
|
1403.4357 | High Speed Railway Wireless Communications: Efficiency v.s. Fairness | cs.IT math.IT | High speed railways (HSRs) have been deployed widely all over the world in
recent years. Different from traditional cellular communication, its high
mobility makes it essential to implement power allocation along the time. In
the HSR case, the transmission rate depends greatly on the distance between the
base station (BS) and the train. As a result, the train receives a time varying
data rate service when passing by a BS. It is clear that the most efficient
power allocation will spend all the power when the train is nearest from the
BS, which will cause great unfairness along the time. On the other hand, the
channel inversion allocation achieves the best fairness in terms of constant
rate transmission. However, its power efficiency is much lower. Therefore, the
power efficiency and the fairness along time are two incompatible objects. For
the HSR cellular system considered in this paper, a trade-off between the two
is achieved by proposing a temporal proportional fair power allocation scheme.
Besides, near optimal closed form solution and one algorithm finding the
$\epsilon$-optimal allocation are presented.
|
1403.4362 | Concept Based vs. Pseudo Relevance Feedback Performance Evaluation for
Information Retrieval System | cs.IR cs.CL | This article evaluates the performance of two techniques for query
reformulation in a system for information retrieval, namely, the concept based
and the pseudo relevance feedback reformulation. The experiments performed on a
corpus of Arabic text have allowed us to compare the contribution of these two
reformulation techniques in improving the performance of an information
retrieval system for Arabic texts.
|
1403.4374 | Low-Complexity Transmission Mode Selection in MU-MIMO Systems | cs.IT math.IT | We propose a low-complexity transmission strategy in multi-user
multiple-input multiple-output downlink systems. The adaptive strategy adjusts
the precoding methods, denoted as the transmission mode, to improve the system
sum rates while maintaining the number of simultaneously served users. Three
linear precoding transmission modes are discussed, i.e., the block
diagonalization zero-forcing, the cooperative zero-forcing (CZF), and the
cooperative matched-filter (CMF). Considering both the number of data streams
and the multiple-antenna configuration of users, we modify the common CZF and
CMF modes by allocating data streams. Then, the transmission mode is selected
between the modified ones according to the asymptotic sum rate analyses. As
instantaneous channel state information is not needed for the mode selection,
the computational complexity is significantly reduced. Numerical simulations
confirm our analyses and demonstrate that the proposed scheme achieves
substantial performance gains with very low computational complexity.
|
1403.4378 | Spectral Clustering with Jensen-type kernels and their multi-point
extensions | cs.LG | Motivated by multi-distribution divergences, which originate in information
theory, we propose a notion of `multi-point' kernels, and study their
applications. We study a class of kernels based on Jensen type divergences and
show that these can be extended to measure similarity among multiple points. We
study tensor flattening methods and develop a multi-point (kernel) spectral
clustering (MSC) method. We further emphasize on a special case of the proposed
kernels, which is a multi-point extension of the linear (dot-product) kernel
and show the existence of cubic time tensor flattening algorithm in this case.
Finally, we illustrate the usefulness of our contributions using standard data
sets and image segmentation tasks.
|
1403.4405 | Absorbing Set Analysis and Design of LDPC Codes from Transversal Designs
over the AWGN Channel | cs.IT math.CO math.IT | In this paper we construct low-density parity-check (LDPC) codes from
transversal designs with low error-floors over the additive white Gaussian
noise (AWGN) channel. The constructed codes are based on transversal designs
that arise from sets of mutually orthogonal Latin squares (MOLS) with cyclic
structure. For lowering the error-floors, our approach is twofold: First, we
give an exhaustive classification of so-called absorbing sets that may occur in
the factor graphs of the given codes. These purely combinatorial substructures
are known to be the main cause of decoding errors in the error-floor region
over the AWGN channel by decoding with the standard sum-product algorithm
(SPA). Second, based on this classification, we exploit the specific structure
of the presented codes to eliminate the most harmful absorbing sets and derive
powerful constraints for the proper choice of code parameters in order to
obtain codes with an optimized error-floor performance.
|
1403.4415 | DecLiNe -- Models for Decay of Links in Networks | cs.SI physics.soc-ph | The prediction of graph evolution is an important and challenging problem in
the analysis of networks and of the Web in particular. But while the appearance
of new links is part of virtually every model of Web growth, the disappearance
of links has received much less attention in the literature. To fill this gap,
our approach DecLiNe (an acronym for DECay of LInks in NEtworks) aims to
predict link decay in networks, based on structural analysis of corresponding
graph models. In analogy to the link prediction problem, we show that analysis
of graph structures can help to identify indicators for superfluous links under
consideration of common network models. In doing so, we introduce novel metrics
that denote the likelihood of certain links in social graphs to remain in the
network, and combine them with state-of-the-art machine learning methods for
predicting link decay. Our methods are independent of the underlying network
type, and can be applied to such diverse networks as the Web, social networks
and any other structure representable as a network, and can be easily combined
with case-specific content analysis and adopted for a variety of social network
mining, filtering and recommendation applications. In systematic evaluations
with large-scale datasets of Wikipedia we show the practical feasibility of the
proposed structure-based link decay prediction algorithms.
|
1403.4452 | The Homogeneous Weight Partition and its Character-Theoretic Dual | cs.IT math.IT math.RA | The values of the normalized homogeneous weight are determined for arbitrary
finite Frobenius rings and expressed in a form that is independent from a
generating character and the M\"obius function on the ring. The weight
naturally induces a partition of the ring, which is invariant under left or
right multiplication by units. It is shown that the character-theoretic
left-sided dual of this partition coincides with the right-sided dual, and even
more, the left- and right-sided Krawtchouk coefficients coincide. An example is
provided showing that this is not the case for general invariant partitions if
the ring is not semisimple.
|
1403.4467 | A hybrid formalism to parse Sign Languages | cs.CL | Sign Language (SL) linguistic is dependent on the expensive task of
annotating. Some automation is already available for low-level information (eg.
body part tracking) and the lexical level has shown significant progresses. The
syntactic level lacks annotated corpora as well as complete and consistent
models. This article presents a solution for the automatic annotation of SL
syntactic elements. It exposes a formalism able to represent both
constituency-based and dependency-based models. The first enable the
representation the structures one may want to annotate, the second aims at
fulfilling the holes of the first. A parser is presented and used to conduct
two experiments on the solution. One experiment is on a real corpus, the other
is on a synthetic corpus.
|
1403.4473 | Sign Language Gibberish for syntactic parsing evaluation | cs.CL | Sign Language (SL) automatic processing slowly progresses bottom-up. The
field has seen proposition to handle the video signal, to recognize and
synthesize sublexical and lexical units. It starts to see the development of
supra-lexical processing. But the recognition, at this level, lacks data. The
syntax of SL appears very specific as it uses massively the multiplicity of
articulators and its access to the spatial dimensions. Therefore new parsing
techniques are developed. However these need to be evaluated. The shortage on
real data restrains the corpus-based models to small sizes. We propose here a
solution to produce data-sets for the evaluation of parsers on the specific
properties of SL. The article first describes the general model used to
generates dependency grammars and the phrase generation from these lasts. It
then discusses the limits of approach. The solution shows to be of particular
interest to evaluate the scalability of the techniques on big models.
|
1403.4482 | SNSAPI: A Cross-Platform Middleware for Rapid Deployment of
Decentralized Social Networks | cs.SI cs.NI | In this paper, we present the design, implementation and our year-long
maintenance experience of SNSAPI, a Python-based middleware which unifies the
interfaces and data structures of heterogeneous Social Networking Services
(SNS). Unlike most prior works, our middleware is user-oriented and requires
zero infrastructure support. It enables a user to readily conduct online social
activities in a programmable, cross-platform fashion while gradually reducing
the dependence on centralized Online Social Networks (OSN). More importantly,
as the SNSAPI middleware can be used to support decentralized social networking
services via conventional communication channels such as RSS or Email, it
enables the deployment of Decentralized Social Networks (DSN) in an
incremental, ad hoc manner. To demonstrate the viability of such type of DSNs,
we have deployed an experimental 6000-node SNSAPI-based DSN on PlanetLab and
evaluate its performance by replaying traces of online social activities
collected from a mainstream OSN. Our results show that, with only mild resource
consumption, the SNSAPI-based DSN can achieve acceptable forwarding latency
comparable to that of a centralized OSN. We also develop an analytical model to
characterize the trade-offs between resource consumption and message forwarding
delay in our DSN. Via 20 parameterized experiments on PlanetLab, we have found
that the empirical measurement results match reasonably with the performance
predicted by our analytical model.
|
1403.4492 | Joint Beamforming Design and Time Allocation for Wireless Powered
Communication Networks | cs.IT math.IT | This paper investigates a multi-input single-output (MISO) wireless powered
communication network (WPCN) under the protocol of harvest-then-transmit. The
power station (PS) with reliable power supply can replenish the passive user
nodes by wireless power transfer (WPT) in the downlink (DL), then each user
node transmits independent information to the sink by a time division multiple
access (TDMA) scheme in the uplink (UL). We consider the joint time allocation
and beamforming design to maximize the system sum-throughput. The semidefinite
relaxation (SDR) technique is applied to solve the nonconvex design problem.
The tightness of SDR approximation, thus the global optimality, is proved. This
implies that only one single energy beamformer is required at the PS. Then a
fast semiclosed form solution is proposed by exploiting the inherent structure.
Simulation results demonstrate the efficiency of the proposed algorithms from
the perspectives of time complexity and information throughput.
|
1403.4508 | Research on Study Mechanical Vibrations with Data Acquisition Systems | cs.CE | The paper presents a new study method of mechanic vibrations with the help of
the data acquisition systems. The study of vibrations with the help of data
acquisition systems allows the solving of some engineering problems connected
to the measurement of some parameters which are difficult to measure having in
view the improvement of the technical performances of the industrial equipment
or devices
|
1403.4514 | Simultaneous Perturbation Algorithms for Batch Off-Policy Search | math.OC cs.LG | We propose novel policy search algorithms in the context of off-policy, batch
mode reinforcement learning (RL) with continuous state and action spaces. Given
a batch collection of trajectories, we perform off-line policy evaluation using
an algorithm similar to that by [Fonteneau et al., 2010]. Using this
Monte-Carlo like policy evaluator, we perform policy search in a class of
parameterized policies. We propose both first order policy gradient and second
order policy Newton algorithms. All our algorithms incorporate simultaneous
perturbation estimates for the gradient as well as the Hessian of the
cost-to-go vector, since the latter is unknown and only biased estimates are
available. We demonstrate their practicality on a simple 1-dimensional
continuous state space problem.
|
1403.4521 | Efficient Network Generation Under General Preferential Attachment | cs.SI cs.DS physics.soc-ph | Preferential attachment (PA) models of network structure are widely used due
to their explanatory power and conceptual simplicity. PA models are able to
account for the scale-free degree distributions observed in many real-world
large networks through the remarkably simple mechanism of sequentially
introducing nodes that attach preferentially to high-degree nodes. The ability
to efficiently generate instances from PA models is a key asset in
understanding both the models themselves and the real networks that they
represent. Surprisingly, little attention has been paid to the problem of
efficient instance generation. In this paper, we show that the complexity of
generating network instances from a PA model depends on the preference function
of the model, provide efficient data structures that work under any preference
function, and present empirical results from an implementation based on these
data structures. We demonstrate that, by indexing growing networks with a
simple augmented heap, we can implement a network generator which scales many
orders of magnitude beyond existing capabilities ($10^6$ -- $10^8$ nodes). We
show the utility of an efficient and general PA network generator by
investigating the consequences of varying the preference functions of an
existing model. We also provide "quicknet", a freely-available open-source
implementation of the methods described in this work.
|
1403.4540 | Similarity networks for classification: a case study in the Horse Colic
problem | cs.LG cs.NE | This paper develops a two-layer neural network in which the neuron model
computes a user-defined similarity function between inputs and weights. The
neuron transfer function is formed by composition of an adapted logistic
function with the mean of the partial input-weight similarities. The resulting
neuron model is capable of dealing directly with variables of potentially
different nature (continuous, fuzzy, ordinal, categorical). There is also
provision for missing values. The network is trained using a two-stage
procedure very similar to that used to train a radial basis function (RBF)
neural network. The network is compared to two types of RBF networks in a
non-trivial dataset: the Horse Colic problem, taken as a case study and
analyzed in detail.
|
1403.4583 | An Achievable rate region for the $3-$user interference channel based on
coset codes | cs.IT math.IT | We consider the problem of communication over a three user discrete
memoryless interference channel ($3-$IC). The current known coding techniques
for communicating over an arbitrary $3-$IC are based on message splitting,
superposition coding and binning using independent and identically distributed
(iid) random codebooks. In this work, we propose a new ensemble of codes -
partitioned coset codes (PCC) - that possess an appropriate mix of empirical
and algebraic closure properties. We develop coding techniques that exploit
algebraic closure property of PCC to enable efficient communication over
$3-$IC. We analyze the performance of the proposed coding technique to derive
an achievable rate region for the general discrete $3-$IC. Additive and
non-additive examples are identified for which the derived achievable rate
region is the capacity, and moreover, strictly larger than current known
largest achievable rate regions based on iid random codebooks.
|
1403.4597 | Energy-Efficient Transmission Schedule for Delay-Limited Bursty Data
Arrivals under Non-Ideal Circuit Power Consumption | cs.IT math.IT | This paper develops a novel approach to obtaining energy-efficient
transmission schedules for delay-limited bursty data arrivals under non-ideal
circuit power consumption. Assuming a-prior knowledge of packet arrivals,
deadlines and channel realizations, we show that the problem can be formulated
as a convex program. For both time-invariant and time-varying fading channels,
it is revealed that the optimal transmission between any two consecutive
channel or data state changing instants, termed epoch, can only take one of the
three strategies: (i) no transmission, (ii) transmission with an
energy-efficiency (EE) maximizing rate over part of the epoch, or (iii)
transmission with a rate greater than the EE-maximizing rate over the whole
epoch. Based on this specific structure, efficient algorithms are then
developed to find the optimal policies that minimize the total energy
consumption with a low computational complexity. The proposed approach can
provide the optimal benchmarks for practical schemes designed for transmissions
of delay-limited data arrivals, and can be employed to develop efficient online
scheduling schemes which require only causal knowledge of data arrivals and
deadline requirements.
|
1403.4608 | Can Cascades be Predicted? | cs.SI physics.soc-ph stat.ML | On many social networking web sites such as Facebook and Twitter, resharing
or reposting functionality allows users to share others' content with their own
friends or followers. As content is reshared from user to user, large cascades
of reshares can form. While a growing body of research has focused on analyzing
and characterizing such cascades, a recent, parallel line of work has argued
that the future trajectory of a cascade may be inherently unpredictable. In
this work, we develop a framework for addressing cascade prediction problems.
On a large sample of photo reshare cascades on Facebook, we find strong
performance in predicting whether a cascade will continue to grow in the
future. We find that the relative growth of a cascade becomes more predictable
as we observe more of its reshares, that temporal and structural features are
key predictors of cascade size, and that initially, breadth, rather than depth
in a cascade is a better indicator of larger cascades. This prediction
performance is robust in the sense that multiple distinct classes of features
all achieve similar performance. We also discover that temporal features are
predictive of a cascade's eventual shape. Observing independent cascades of the
same content, we find that while these cascades differ greatly in size, we are
still able to predict which ends up the largest.
|
1403.4640 | Communication Communities in MOOCs | cs.CY cs.SI stat.ML | Massive Open Online Courses (MOOCs) bring together thousands of people from
different geographies and demographic backgrounds -- but to date, little is
known about how they learn or communicate. We introduce a new content-analysed
MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to
extract communities of learners based on the nature of their online forum
posts. We see that BNMF yields a superior probabilistic generative model for
online discussions when compared to other models, and that the communities it
learns are differentiated by their composite students' demographic and course
performance indicators. These findings suggest that computationally efficient
probabilistic generative modelling of MOOCs can reveal important insights for
educational researchers and practitioners and help to develop more intelligent
and responsive online learning environments.
|
1403.4643 | Information Content of Systems as a Physical Principle | quant-ph cs.IT math.IT | To explain conceptual gap between classical/quantum and other, hypothetical
descriptions of world, several principles has been proposed. So far, all these
principles have not explicitly included the uncertainty relation. Here we
introduce an information content principle (ICP) which represents the new -
constrained uncertainty principle. The principle, by taking into account the
encoding/decoding properties of single physical system, is capable of
separation both classicality and quanta from a number of potential physical
theories including hidden variable theories. The ICP, which is satisfied by
both classical and quantum theory, states that the amount of non-redundant
information which may be extracted from a given system is bounded by a
perfectly decodable information content of the system. We show that ICP allows
to discriminate theories which do not allow for correlations stronger than
Tsirelson's bound. We show also how to apply the principle to composite
systems, ruling out some theories despite their elementary constituents behave
quantumly.
|
1403.4655 | Quadrature-Based Vector Fitting: Implications For H2 System
Approximation | math.NA cs.SY | Vector Fitting is a popular method of constructing rational approximants
designed to fit given frequency response measurements. The original method,
which we refer to as VF, is based on a least-squares fit to the measurements by
a rational function, using an iterative reallocation of the poles of the
approximant. We show that one can improve the performance of VF significantly,
by using a particular choice of frequency sampling points and properly
weighting their contribution based on quadrature rules that connect the least
squares objective with an H2 error measure. Our modified approach, designated
here as QuadVF, helps recover the original transfer function with better global
fidelity (as measured with respect to the H2 norm), than the localized least
squares approximation implicit in VF. We extend the new framework also to
incorporate derivative information, leading to rational approximants that
minimize system error with respect to a discrete Sobolev norm. We consider the
convergence behavior of both VF and QuadVF as well, and evaluate potential
numerical ill-conditioning of the underlying least-squares problems. We
investigate briefly VF in the case of noisy measurements and propose a new
formulation for the resulting approximation problem. Several numerical examples
are provided to support the theoretical discussion.
|
1403.4661 | An Optimal-Dimensionality Sampling Scheme on the Sphere with Fast
Spherical Harmonic Transforms | cs.IT math.IT | We develop a sampling scheme on the sphere that permits accurate computation
of the spherical harmonic transform and its inverse for signals band-limited at
$L$ using only $L^2$ samples. We obtain the optimal number of samples given by
the degrees of freedom of the signal in harmonic space. The number of samples
required in our scheme is a factor of two or four fewer than existing
techniques, which require either $2L^2$ or $4L^2$ samples. We note, however,
that we do not recover a sampling theorem on the sphere, where spherical
harmonic transforms are theoretically exact. Nevertheless, we achieve high
accuracy even for very large band-limits. For our optimal-dimensionality
sampling scheme, we develop a fast and accurate algorithm to compute the
spherical harmonic transform (and inverse), with computational complexity
comparable with existing schemes in practice. We conduct numerical experiments
to study in detail the stability, accuracy and computational complexity of the
proposed transforms. We also highlight the advantages of the proposed sampling
scheme and associated transforms in the context of potential applications.
|
1403.4662 | Model Predictive HVAC Control with Online Occupancy Model | cs.SY | This paper presents an occupancy-predicting control algorithm for heating,
ventilation, and air conditioning (HVAC) systems in buildings. It incorporates
the building's thermal properties, local weather predictions, and a self-tuning
stochastic occupancy model to reduce energy consumption while maintaining
occupant comfort. Contrasting with existing approaches, the occupancy model
requires no manual training and adapts to changes in occupancy patterns during
operation. A prediction-weighted cost function provides conditioning of thermal
zones before occupancy begins and reduces system output before occupancy ends.
Simulation results with real-world occupancy data demonstrate the algorithm's
effectiveness.
|
1403.4669 | Performance Analysis of Arbitrarily-Shaped Underlay Cognitive Networks:
Effects of Secondary User Activity Protocols | cs.IT math.IT | This paper analyzes the performance of the primary and secondary users (SUs)
in an arbitrarily-shaped underlay cognitive network. In order to meet the
interference threshold requirement for a primary receiver (PU-Rx) at an
arbitrary location, we consider different SU activity protocols which limit the
number of active SUs. We propose a framework, based on the moment generating
function (MGF) of the interference due to a random SU, to analytically compute
the outage probability in the primary network, as well as the average number of
active SUs in the secondary network. We also propose a cooperation-based SU
activity protocol in the underlay cognitive network which includes the existing
threshold-based protocol as a special case. We study the average number of
active SUs for the different SU activity protocols, subject to a given outage
probability constraint at the PU and we employ it as an analytical approach to
compare the effect of different SU activity protocols on the performance of the
primary and secondary networks.
|
1403.4670 | Quantized Output Feedback Stabilization of Switched Linear Systems | cs.SY math.OC | This paper studies the problem of stabilizing a continuous-time switched
linear system by quantized output feedback. We assume that the quantized
outputs and the switching signal are available to the controller at all time.
We develop an encoding strategy by using multiple Lyapunov functions and an
average dwell time property. The encoding strategy is based on the results in
the case of a single mode, and it requires an additional adjustment of the
"zoom" parameter at every switching time.
|
1403.4679 | Justification of Logarithmic Loss via the Benefit of Side Information | cs.IT math.IT | We consider a natural measure of relevance: the reduction in optimal
prediction risk in the presence of side information. For any given loss
function, this relevance measure captures the benefit of side information for
performing inference on a random variable under this loss function. When such a
measure satisfies a natural data processing property, and the random variable
of interest has alphabet size greater than two, we show that it is uniquely
characterized by the mutual information, and the corresponding loss function
coincides with logarithmic loss. In doing so, our work provides a new
characterization of mutual information, and justifies its use as a measure of
relevance. When the alphabet is binary, we characterize the only admissible
forms the measure of relevance can assume while obeying the specified data
processing property. Our results naturally extend to measuring causal influence
between stochastic processes, where we unify different causal-inference
measures in the literature as instantiations of directed information.
|
1403.4682 | Structured Sparse Method for Hyperspectral Unmixing | cs.CV cs.AI | Hyperspectral Unmixing (HU) has received increasing attention in the past
decades due to its ability of unveiling information latent in hyperspectral
data. Unfortunately, most existing methods fail to take advantage of the
spatial information in data. To overcome this limitation, we propose a
Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method
from the following two aspects. First, we incorporate a graph Laplacian to
encode the manifold structures embedded in the hyperspectral data space. In
this way, the highly similar neighboring pixels can be grouped together.
Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the
same manifold structure are sparsely mixed by a common set of relevant bases.
These two factors act as a new structured sparse constraint. With this
constraint, our method can learn a compact space, where highly similar pixels
are grouped to share correlated sparse representations. Experiments on real
hyperspectral data sets with different noise levels demonstrate that our method
outperforms the state-of-the-art methods significantly.
|
1403.4691 | Quantized Feedback Stabilization of Sampled-Data Switched Linear Systems | cs.SY | We propose a stability analysis method for sampled-data switched linear
systems with quantization. The available information to the controller is
limited: the quantized state and switching signal at each sampling time.
Switching between sampling times can produce the mismatch of the modes between
the plant and the controller. Moreover, the coarseness of quantization makes
the trajectory wander around, not approach, the origin. Hence the trajectory
may leave the desired neighborhood if the mismatch leads to instability of the
closed-loop system. For the stability of the switched systems, we develop a
sufficient condition characterized by the total mismatch time. The relationship
between the mismatch time and the dwell time of the switching signal is also
discussed.
|
1403.4711 | Multiagent Conflict Resolution for a Specification Network of
Discrete-Event Coordinating Agents | cs.MA | This paper presents a novel compositional approach to distributed
coordination module (CM) synthesis for multiple discrete-event agents in the
formal languages and automata framework. The approach is supported by two
original ideas. The first is a new formalism called the Distributed Constraint
Specification Network (DCSN) that can comprehensibly describe the networking
constraint relationships among distributed agents. The second is multiagent
conflict resolution planning, which entails generating and using AND/OR graphs
to compactly represent conflict resolution (synthesis-process) plans for a
DCSN. Together with the framework of local CM design developed in the authors'
earlier work, the systematic approach supports separately designing local and
deconflicting CM's for individual agents in accordance to a selected conflict
resolution plan. Composing the agent models and the CM's designed furnishes an
overall nonblocking coordination solution that meets the set of inter-agent
constraints specified in a given DCSN.
|
1403.4714 | Enhancing Dictionary Based Preprocessing For Better Text Compression | cs.IT math.IT | With the rapid growing of data and number of applications, there is a crucial
need of dictionary based reversible transformation techniques to increase the
efficiency of the compression algorithms and hence contribute towards the
enhancement in compression ratio. Performance analysis of compression methods
in combination with the various transformation techniques is obtained for
different text files of varying sizes. The popular block sorting lossless
Burrows Wheeler Compression Algorithm (BWCA) is implemented along with one
proposed method. For efficient compression a dictionary based transformation
algorithm is also developed. It is observed that much increase in terms of
compression ratio is attained when a source file is preprocessed with
dictionary and then applied to BWCA and the proposed method.
|
1403.4735 | On the Automorphisms of Order 15 for a Binary Self-Dual [96, 48, 20]
Code | cs.IT math.IT | The structure of binary self-dual codes invariant under the action of a
cyclic group of order $pq$ for odd primes $p\neq q$ is considered. As an
application we prove the nonexistence of an extremal self-dual $[96, 48, 20]$
code with an automorphism of order $15$ which closes a gap in `"On extremal
self-dual codes of length 96", IEEE Trans. Inf. Theory, vol. 57, pp. 6820-6823,
2011'.
|
1403.4751 | The Deterministic Time-Linearity of Service Provided by Fading Channels | cs.IT math.IT | In the paper, we study the service process $S(t)$ of an independent and
identically distributed (\textit{i.i.d.}) Nakagami-$m$ fading channel, which is
defined as the amount of service provided, i.e., the integral of the
instantaneous channel capacity over time $t$. By using the Characteristic
Function (CF) approach and the infinitely divisible law, it is proved that,
other than certain generally recognized curve form {or a stochastic process},
the channel service process $S(t)$ is a deterministic linear function of time
$t$, namely, $S(t)=c_m^\ast\cdot t$ where $c_m^\ast$ is a constant determined
by the fading parameter $m$. Furthermore, we extend it to general
\textit{i.i.d.} fading channels and present an explicit form of the constant
service rate $c_p^\ast$. The obtained work provides such a new insight on the
system design of joint source/channel coding that there exists a coding scheme
such that a receiver can decode with zero error probability and zero high layer
queuing delay, if the transmitter maintains a constant data rate no more than
$c_p^\ast$. Finally, we verify our analysis through Monte Carlo simulations.
|
1403.4759 | Spelling Error Trends and Patterns in Sindhi | cs.CL | Statistical error Correction technique is the most accurate and widely used
approach today, but for a language like Sindhi which is a low resourced
language the trained corpora's are not available, so the statistical techniques
are not possible at all. Instead a useful alternative would be to exploit
various spelling error trends in Sindhi by using a Rule based approach. For
designing such technique an essential prerequisite would be to study the
various error patterns in a language. This pa per presents various studies of
spelling error trends and their types in Sindhi Language. The research shows
that the error trends common to all languages are also encountered in Sindhi
but their do exist some error patters that are catered specifically to a Sindhi
language.
|
1403.4762 | Maximally Permissive Coordination Supervisory Control -- Towards
Necessary and Sufficient Conditions | math.OC cs.FL cs.SY | In this paper, we further develop the coordination control framework for
discrete-event systems with both complete and partial observation. A new weaker
sufficient condition for the computation of the supremal conditionally
controllable sublanguage is presented. This result is then used for the
computation of the supremal conditionally controllable and conditionally normal
sublanguage. The paper further generalizes the previous study by considering
general, non-prefix-closed languages.
|
1403.4777 | MFCC based Enlargement of the Training Set for Emotion Recognition in
Speech | cs.CV | Emotional state recognition through speech is being a very interesting
research topic nowadays. Using subliminal information of speech, denominated as
prosody, it is possible to recognize the emotional state of the person. One of
the main problems in the design of automatic emotion recognition systems is the
small number of available patterns. This fact makes the learning process more
difficult, due to the generalization problems that arise under these
conditions. In this work we propose a solution to this problem consisting in
enlarging the training set through the creation the new virtual patterns. In
the case of emotional speech, most of the emotional information is included in
speed and pitch variations. So, a change in the average pitch that does not
modify neither the speed nor the pitch variations does not affect the expressed
emotion. Thus, we use this prior information in order to create new patterns
applying a gender dependent pitch shift modification in the feature extraction
process of the classification system. For this purpose, we propose a frequency
scaling modification of the Mel Frequency Cepstral Coefficients, used to
classify the emotion. For this purpose, we propose a gender dependent frequency
scaling modification. This proposed process allows us to synthetically increase
the number of available patterns in the training set, thus increasing the
generalization capability of the system and reducing the test error. Results
carried out with two different classifiers with different degree of
generalization capability demonstrate the suitability of the proposal.
|
1403.4781 | A Split-and-Merge Dictionary Learning Algorithm for Sparse
Representation | cs.LG stat.ML | In big data image/video analytics, we encounter the problem of learning an
overcomplete dictionary for sparse representation from a large training
dataset, which can not be processed at once because of storage and
computational constraints. To tackle the problem of dictionary learning in such
scenarios, we propose an algorithm for parallel dictionary learning. The
fundamental idea behind the algorithm is to learn a sparse representation in
two phases. In the first phase, the whole training dataset is partitioned into
small non-overlapping subsets, and a dictionary is trained independently on
each small database. In the second phase, the dictionaries are merged to form a
global dictionary. We show that the proposed algorithm is efficient in its
usage of memory and computational complexity, and performs on par with the
standard learning strategy operating on the entire data at a time. As an
application, we consider the problem of image denoising. We present a
comparative analysis of our algorithm with the standard learning techniques,
that use the entire database at a time, in terms of training and denoising
performance. We observe that the split-and-merge algorithm results in a
remarkable reduction of training time, without significantly affecting the
denoising performance.
|
1403.4789 | Structure-preserving model reduction of physical network systems by
clustering | cs.SY | In this paper, we establish a method for model order reduction of a certain
class of physical network systems. The proposed method is based on clustering
of the vertices of the underlying graph, and yields a reduced order model
within the same class. To capture the physical properties of the network, we
allow for weights associated to both the edges as well as the vertices of the
graph. We extend the notion of almost equitable partitions to this class of
graphs. Consequently, an explicit model reduction error expression in the sense
of H2-norm is provided for clustering arising from almost equitable partitions.
Finally the method is extended to second-order systems.
|
1403.4804 | Distributed System Identification with ADMM | cs.SY | This paper presents identification of both network connected systems as well
as distributed systems governed by PDEs in the framework of distributed
optimization via the Alternating Direction Method of Multipliers. This approach
opens first the possibility to identify distributed models in a global manner
using all available data sequences and second the possibility for a distributed
implementation. The latter will make the application to large scale complex
systems possible. In addition to outlining a new large scale identification
method, illustrations are shown for identifying both network connected systems
and discretized PDEs.
|
1403.4828 | Optimal Provision of Regulation Service Reserves Under Dynamic Energy
Service Preferences | cs.SY | We propose and solve a stochastic dynamic programming (DP) problem addressing
the optimal provision of regulation service reserves (RSR) by controlling
dynamic demand preferences in smart buildings. A major contribution over past
dynamic pricing work is that we pioneer the relaxation of static, uniformly
distributed utility of demand. In this paper we model explicitly the dynamics
of energy service preferences leading to a non-uniform and time varying
probability distribution of demand utility. More explicitly, we model active
and idle duty cycle appliances in a smart building as a closed queuing system
with price-controlled arrival rates into the active appliance queue. Focusing
on cooling appliances, we model the utility associated with the transition from
idle to active as a non-uniform time varying function. We (i) derive an
analytic characterization of the optimal policy and the differential cost
function, and (ii) prove optimal policy monotonicity and value function
convexity. These properties enable us to propose and implement a smart assisted
value iteration (AVI) algorithm and an approximate DP (ADP) that exploits
related functional approximations. Numerical results demonstrate the validity
of the solution techniques and the computational advantage of the proposed ADP
on realistic, large-state-space problems.
|
1403.4847 | Massive MIMO Systems with Hardware-Constrained Base Stations | cs.IT math.IT | Massive multiple-input multiple-output (MIMO) systems are cellular networks
where the base stations (BSs) are equipped with unconventionally many antennas.
Such large antenna arrays offer huge spatial degrees-of-freedom for
transmission optimization; in particular, great signal gains, resilience to
imperfect channel knowledge, and small inter-user interference are all
achievable without extensive inter-cell coordination. The key to cost-efficient
deployment of large arrays is the use of hardware-constrained base stations
with low-cost antenna elements, as compared to today's expensive and
power-hungry BSs. Low-cost transceivers are prone to hardware imperfections,
but it has been conjectured that the excessive degrees-of-freedom of massive
MIMO would bring robustness to such imperfections. We herein prove this claim
for an uplink channel with multiplicative phase-drift, additive distortion
noise, and noise amplification. Specifically, we derive a closed-form scaling
law that shows how fast the imperfections increase with the number of antennas.
|
1403.4851 | Circuit-Aware Design of Energy-Efficient Massive MIMO Systems | cs.IT math.IT | Densification is a key to greater throughput in cellular networks. The full
potential of coordinated multipoint (CoMP) can be realized by massive
multiple-input multiple-output (MIMO) systems, where each base station (BS) has
very many antennas. However, the improved throughput comes at the price of more
infrastructure; hardware cost and circuit power consumption scale
linearly/affinely with the number of antennas. In this paper, we show that one
can make the circuit power increase with only the square root of the number of
antennas by circuit-aware system design. To this end, we derive achievable user
rates for a system model with hardware imperfections and show how the level of
imperfections can be gradually increased while maintaining high throughput. The
connection between this scaling law and the circuit power consumption is
established for different circuits at the BS.
|
1403.4868 | Zero forcing sets and controllability of dynamical systems defined on
graphs | cs.SY | In this paper, controllability of systems defined on graphs is discussed. We
consider the problem of controllability of the network for a family of matrices
carrying the structure of an underlying directed graph. A one-to-one
correspondence between the set of leaders rendering the network controllable
and zero forcing sets is established. To illustrate the proposed results,
special cases including path, cycle, and complete graphs are discussed.
Moreover, as shown for graphs with a tree structure, the proposed results of
the present paper together with the existing results on the zero forcing sets
lead to a minimal leader selection scheme in particular cases.
|
1403.4871 | Evolutionary Algorithm for Drug Discovery Interim Design Report | cs.NE cs.CE | A software program which aims to provide an exploration capability over the
Search Space of potential drug molecules. The program explores the search space
by generating random molecules, determining their fitness and then breeding a
new generation from the fittest individuals. The search space, in theory any
combination of any elements in any order, is constrained by the use of a subset
of elements and a list of fragments, molecular parts that are known to be
useful in drug development. The resultant molecules from each generation are
stored in a searchable database, so that the user can browse through previous
generations looking for interesting molecules.
|
1403.4879 | A Compressive Sensing Based Approach to Sparse Wideband Array Design | cs.IT math.IT math.OC | Sparse wideband sensor array design for sensor location optimisation is
highly nonlinear and it is traditionally solved by genetic algorithms,
simulated annealing or other similar optimization methods. However, this is an
extremely time-consuming process and more efficient solutions are needed. In
this work, this problem is studied from the viewpoint of compressive sensing
and a formulation based on a modified $l_1$ norm is derived. As there are
multiple coefficients associated with each sensor, the key is to make sure that
these coefficients are simultaneously minimized in order to discard the
corresponding sensor locations. Design examples are provided to verify the
effectiveness of the proposed methods.
|
1403.4881 | Comparing Numerical Integration Schemes for Time-Continuous
Car-Following Models | cs.CE physics.soc-ph | When simulating trajectories by integrating time-continuous car-following
models, standard integration schemes such as the forth-order Runge-Kutta method
(RK4) are rarely used while the simple Euler's method is popular among
researchers. We compare four explicit methods: Euler's method, ballistic
update, Heun's method (trapezoidal rule), and the standard forth-order RK4. As
performance metrics, we plot the global discretization error as a function of
the numerical complexity. We tested the methods on several time-continuous
car-following models in several multi-vehicle simulation scenarios with and
without discontinuities such as stops or a discontinuous behavior of an
external leader. We find that the theoretical advantage of RK4 (consistency
order~4) only plays a role if both the acceleration function of the model and
the external data of the simulation scenario are sufficiently often
differentiable. Otherwise, we obtain lower (and often fractional) consistency
orders. Although, to our knowledge, Heun's method has never been used for
integrating car-following models, it turns out to be the best scheme for many
practical situations. The ballistic update always prevails Euler's method
although both are of first order.
|
1403.4887 | Using Entropy Estimates for DAG-Based Ontologies | cs.CL | Motivation: Entropy measurements on hierarchical structures have been used in
methods for information retrieval and natural language modeling. Here we
explore its application to semantic similarity. By finding shared ontology
terms, semantic similarity can be established between annotated genes. A common
procedure for establishing semantic similarity is to calculate the
descriptiveness (information content) of ontology terms and use these values to
determine the similarity of annotations. Most often information content is
calculated for an ontology term by analyzing its frequency in an annotation
corpus. The inherent problems in using these values to model functional
similarity motivates our work. Summary: We present a novel calculation for
establishing the entropy of a DAG-based ontology, which can be used in an
alternative method for establishing the information content of its terms. We
also compare our IC metric to two others using semantic and sequence
similarity.
|
1403.4891 | Dynamics of social balance under temporal interaction | cs.SI physics.soc-ph | Real social contacts are often intermittent such that a link between a pair
of nodes in a social network is only temporarily used. Effects of such temporal
networks on social dynamics have been investigated for several phenomenological
models such as epidemic spreading, linear diffusion processes, and nonlinear
oscillations. Here, we numerically investigate nonlinear social balance
dynamics in such a situation. Social balance is a classical psychological
theory, which dictates that a triad is balanced if the three agents are mutual
friends or if the two of them are the friends of each other and hostile to the
other agent. We show that the social balance dynamics is slowed down on the
temporal complete graph as compared to the corresponding static complete graph.
|
1403.4928 | Clinical TempEval | cs.CL | We describe the Clinical TempEval task which is currently in preparation for
the SemEval-2015 evaluation exercise. This task involves identifying and
describing events, times and the relations between them in clinical text. Six
discrete subtasks are included, focusing on recognising mentions of times and
events, describing those mentions for both entity types, identifying the
relation between an event and the document creation time, and identifying
narrative container relations.
|
1403.4997 | Universal and Distinct Properties of Communication Dynamics: How to
Generate Realistic Inter-event Times | cs.SI cs.LG physics.soc-ph | With the advancement of information systems, means of communications are
becoming cheaper, faster and more available. Today, millions of people carrying
smart-phones or tablets are able to communicate at practically any time and
anywhere they want. Among others, they can access their e-mails, comment on
weblogs, watch and post comments on videos, make phone calls or text messages
almost ubiquitously. Given this scenario, in this paper we tackle a fundamental
aspect of this new era of communication: how the time intervals between
communication events behave for different technologies and means of
communications? Are there universal patterns for the inter-event time
distribution (IED)? In which ways inter-event times behave differently among
particular technologies? To answer these questions, we analyze eight different
datasets from real and modern communication data and we found four well defined
patterns that are seen in all the eight datasets. Moreover, we propose the use
of the Self-Feeding Process (SFP) to generate inter-event times between
communications. The SFP is extremely parsimonious point process that requires
at most two parameters and is able to generate inter-event times with all the
universal properties we observed in the data. We show the potential application
of SFP by proposing a framework to generate a synthetic dataset containing
realistic communication events of any one of the analyzed means of
communications (e.g. phone calls, e-mails, comments on blogs) and an algorithm
to detect anomalies.
|
1403.5006 | Generating Preview Tables for Entity Graphs | cs.DB cs.IR | Users are tapping into massive, heterogeneous entity graphs for many
applications. It is challenging to select entity graphs for a particular need,
given abundant datasets from many sources and the oftentimes scarce information
for them. We propose methods to produce preview tables for compact presentation
of important entity types and relationships in entity graphs. The preview
tables assist users in attaining a quick and rough preview of the data. They
can be shown in a limited display space for a user to browse and explore,
before she decides to spend time and resources to fetch and investigate the
complete dataset. We formulate several optimization problems that look for
previews with the highest scores according to intuitive goodness measures,
under various constraints on preview size and distance between preview tables.
The optimization problem under distance constraint is NP-hard. We design a
dynamic-programming algorithm and an Apriori-style algorithm for finding
optimal previews. Results from experiments, comparison with related work and
user studies demonstrated the scoring measures' accuracy and the discovery
algorithms' efficiency.
|
1403.5020 | State-space solution to a minimum-entropy $\mathcal{H}_\infty$-optimal
control problem with a nested information constraint | cs.SY math.OC | State-space formulas are derived for the minimum-entropy $\mathcal{H}_\infty$
controller when the plant and controller are constrained to be
block-lower-triangular. Such a controller exists if and only if: the
corresponding unstructured problem has a solution, a certain pair of coupled
algebraic Riccati equations admits a mutually stabilizing fixed point, and a
pair of spectral radius conditions is met. The controller's observer-based
structure is also discussed, and a simple numerical approach for solving the
coupled Riccati equations is presented.
|
1403.5022 | An efficient, variational approximation of the best fitting
multi-Bernoulli filter | cs.SY | The joint probabilistic data association (JPDA) filter is a popular tracking
methodology for problems involving well-spaced targets, but it is rarely
applied in problems with closely-spaced targets due to its complexity in these
cases, and due to the well-known phenomenon of coalescence. This paper
addresses these difficulties using random finite sets (RFSs) and variational
inference, deriving a highly tractable, approximate method for obtaining the
multi-Bernoulli distribution that minimises the set Kullback-Leibler (KL)
divergence from the true posterior, working within the RFS framework to
incorporate uncertainty in target existence. The derivation is interpreted as
an application of expectation-maximisation (EM), where the missing data is the
correspondence of Bernoulli components (i.e., tracks) under each data
association hypothesis. The missing data is shown to play an identical role to
the selection of an ordered distribution in the same ordered family in the set
JPDA algorithm. Subsequently, a special case of the proposed method is utilised
to provide an efficient approximation of the minimum mean optimal sub-pattern
assignment estimator. The performance of the proposed methods is demonstrated
in challenging scenarios in which up to twenty targets come into close
proximity.
|
1403.5029 | Network-based Isoform Quantification with RNA-Seq Data for Cancer
Transcriptome Analysis | cs.CE cs.AI cs.LG | High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript
quantification of gene isoforms. Since RNA-Seq data alone is often not
sufficient to accurately identify the read origins from the isoforms for
quantification, we propose to explore protein domain-domain interactions as
prior knowledge for integrative analysis with RNA-seq data. We introduce a
Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to
integrate protein domain-domain interaction network with short read alignments
for transcript abundance estimation. Based on our observation that the
abundances of the neighboring isoforms by domain-domain interactions in the
network are positively correlated, Net-RSTQ models the expression of the
neighboring transcripts as Dirichlet priors on the likelihood of the observed
read alignments against the transcripts in one gene. The transcript abundances
of all the genes are then jointly estimated with alternating optimization of
multiple EM problems. In simulation Net-RSTQ effectively improved isoform
transcript quantifications when isoform co-expressions correlate with their
interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an
ovarian cancer cell line, and a breast cancer cell line also showed that
Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In
the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the
transcript abundances estimated by Net-RSTQ are more informative for patient
sample classification of ovarian cancer, breast cancer and lung cancer. All
experimental results collectively support that Net-RSTQ is a promising approach
for isoform quantification.
|
1403.5045 | Matroid Bandits: Fast Combinatorial Optimization with Learning | cs.LG cs.AI cs.SY stat.ML | A matroid is a notion of independence in combinatorial optimization which is
closely related to computational efficiency. In particular, it is well known
that the maximum of a constrained modular function can be found greedily if and
only if the constraints are associated with a matroid. In this paper, we bring
together the ideas of bandits and matroids, and propose a new class of
combinatorial bandits, matroid bandits. The objective in these problems is to
learn how to maximize a modular function on a matroid. This function is
stochastic and initially unknown. We propose a practical algorithm for solving
our problem, Optimistic Matroid Maximization (OMM); and prove two upper bounds,
gap-dependent and gap-free, on its regret. Both bounds are sublinear in time
and at most linear in all other quantities of interest. The gap-dependent upper
bound is tight and we prove a matching lower bound on a partition matroid
bandit. Finally, we evaluate our method on three real-world problems and show
that it is practical.
|
1403.5071 | Organised crime infiltration in the legitimate private economy - An
empirical network analysis approach | cs.CY cs.SI physics.soc-ph | It is estimated that Italian Mafias registered 135 billion euros in profits
only in 2010. Part of this huge amount of money, coming mostly from the drugs,
prostitution and arms illicit markets, is often used to invest into legitimate
private economies. As a consequence, the affected economies destabilise, become
entrenched with violent forms of competition and are bound to stagnation.
Nonetheless, few are the attempts to uncover the patterns followed by criminal
organisations in their business ventures. The reason lays mostly in the poor
availability of data on criminal activity, or in the highly risky task of
gather it.
This paper partially fills this gap thanks to access to information about the
Sicilian Mafia in a city. More specifically, it tries to analyse the nature and
extent of criminal infiltration into the legitimate private economy of the
case-study using network techniques. The research demonstrates that sectors
with a high degree of centrality and comprising fewer firms are the most
vulnerable to this kind of security threat. It also shows that centrality is
also the key criterion that makes a firm sensitive to infiltration, provided it
belongs to a susceptible economic sector.
|
1403.5089 | On the Gaussian Many-to-One X Channel | cs.IT math.IT | In this paper, the Gaussian many-to-one X channel, which is a special case of
general multiuser X channel, is studied. In the Gaussian many-to-one X channel,
communication links exist between all transmitters and one of the receivers,
along with a communication link between each transmitter and its corresponding
receiver. As per the X channel assumption, transmission of messages is allowed
on all the links of the channel. This communication model is different from the
corresponding many-to-one interference channel (IC). Transmission strategies
which involve using Gaussian codebooks and treating interference from a subset
of transmitters as noise are formulated for the above channel. Sum-rate is used
as the criterion of optimality for evaluating the strategies. Initially, a $3
\times 3$ many-to-one X channel is considered and three transmission strategies
are analyzed. The first two strategies are shown to achieve sum-rate capacity
under certain channel conditions. For the third strategy, a sum-rate outer
bound is derived and the gap between the outer bound and the achieved rate is
characterized. These results are later extended to the $K \times K$ case. Next,
a region in which the many-to-one X channel can be operated as a many-to-one IC
without loss of sum-rate is identified. Further, in the above region, it is
shown that using Gaussian codebooks and treating interference as noise achieves
a rate point that is within $K/2 -1$ bits from the sum-rate capacity.
Subsequently, some implications of the above results to the Gaussian
many-to-one IC are discussed. Transmission strategies for the many-to-one IC
are formulated and channel conditions under which the strategies achieve
sum-rate capacity are obtained. A region where the sum-rate capacity can be
characterized to within $K/2-1$ bits is also identified.
|
1403.5115 | Unconfused Ultraconservative Multiclass Algorithms | cs.LG | We tackle the problem of learning linear classifiers from noisy datasets in a
multiclass setting. The two-class version of this problem was studied a few
years ago by, e.g. Bylander (1994) and Blum et al. (1996): in these
contributions, the proposed approaches to fight the noise revolve around a
Perceptron learning scheme fed with peculiar examples computed through a
weighted average of points from the noisy training set. We propose to build
upon these approaches and we introduce a new algorithm called UMA (for
Unconfused Multiclass additive Algorithm) which may be seen as a generalization
to the multiclass setting of the previous approaches. In order to characterize
the noise we use the confusion matrix as a multiclass extension of the
classification noise studied in the aforementioned literature. Theoretically
well-founded, UMA furthermore displays very good empirical noise robustness, as
evidenced by numerical simulations conducted on both synthetic and real data.
Keywords: Multiclass classification, Perceptron, Noisy labels, Confusion Matrix
|
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