id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
1303.2176 | Scaling behavior of online human activity | physics.soc-ph cs.SI | The rapid development of Internet technology enables human explore the web
and record the traces of online activities. From the analysis of these
large-scale data sets (i.e. traces), we can get insights about dynamic behavior
of human activity. In this letter, the scaling behavior and complexity of human
activity in the e-commerce, such as music, book, and movie rating, are
comprehensively investigated by using detrended fluctuation analysis technique
and multiscale entropy method. Firstly, the interevent time series of rating
behaviors of these three type medias show the similar scaling property with
exponents ranging from 0.53 to 0.58, which implies that the collective
behaviors of rating media follow a process embodying self-similarity and
long-range correlation. Meanwhile, by dividing the users into three groups
based their activities (i.e., rating per unit time), we find that the scaling
exponents of interevent time series in three groups are different. Hence, these
results suggest the stronger long-range correlations exist in these collective
behaviors. Furthermore, their information complexities vary from three groups.
To explain the differences of the collective behaviors restricted to three
groups, we study the dynamic behavior of human activity at individual level,
and find that the dynamic behaviors of a few users have extremely small scaling
exponents associating with long-range anticorrelations. By comparing with the
interevent time distributions of four representative users, we can find that
the bimodal distributions may bring the extraordinary scaling behaviors. These
results of analyzing the online human activity in the e-commerce may not only
provide insights to understand its dynamic behaviors but also be applied to
acquire the potential economic interest.
|
1303.2184 | Complex Support Vector Machines for Regression and Quaternary
Classification | cs.LG stat.ML | The paper presents a new framework for complex Support Vector Regression as
well as Support Vector Machines for quaternary classification. The method
exploits the notion of widely linear estimation to model the input-out relation
for complex-valued data and considers two cases: a) the complex data are split
into their real and imaginary parts and a typical real kernel is employed to
map the complex data to a complexified feature space and b) a pure complex
kernel is used to directly map the data to the induced complex feature space.
The recently developed Wirtinger's calculus on complex reproducing kernel
Hilbert spaces (RKHS) is employed in order to compute the Lagrangian and derive
the dual optimization problem. As one of our major results, we prove that any
complex SVM/SVR task is equivalent with solving two real SVM/SVR tasks
exploiting a specific real kernel which is generated by the chosen complex
kernel. In particular, the case of pure complex kernels leads to the generation
of new kernels, which have not been considered before. In the classification
case, the proposed framework inherently splits the complex space into four
parts. This leads naturally in solving the four class-task (quaternary
classification), instead of the typical two classes of the real SVM. In turn,
this rationale can be used in a multiclass problem as a split-class scenario
based on four classes, as opposed to the one-versus-all method; this can lead
to significant computational savings. Experiments demonstrate the effectiveness
of the proposed framework for regression and classification tasks that involve
complex data.
|
1303.2211 | Medical Information Embedding in Compressed Watermarked Intravascular
Ultrasound Video | cs.MM cs.CV | In medical field, intravascular ultrasound (IVUS) is a tomographic imaging
modality, which can identify the boundaries of different layers of blood
vessels. IVUS can detect myocardial infarction (heart attack) that remains
ignored and unattended when only angioplasty is done. During the past decade,
it became easier for some individuals or groups to copy and transmits digital
information without the permission of the owner. For increasing authentication
and security of copyrights, digital watermarking, an information hiding
technique, was introduced. Achieving watermarking technique with lesser amount
of distortion in biomedical data is a challenging task. Watermark can be
embedded into an image or in a video. As video data is a huge amount of
information, therefore a large storage area is needed which is not feasible. In
this case motion vector based video compression is done to reduce size. In this
present paper, an Electronic Patient Record (EPR) is embedded as watermark
within an IVUS video and then motion vector is calculated. This proposed method
proves robustness as the extracted watermark has good PSNR value and less MSE.
|
1303.2215 | Expensive Optimisation: A Metaheuristics Perspective | cs.NE | Stochastic, iterative search methods such as Evolutionary Algorithms (EAs)
are proven to be efficient optimizers. However, they require evaluation of the
candidate solutions which may be prohibitively expensive in many real world
optimization problems. Use of approximate models or surrogates is being
explored as a way to reduce the number of such evaluations. In this paper we
investigated three such methods. The first method (DAFHEA) partially replaces
an expensive function evaluation by its approximate model. The approximation is
realized with support vector machine (SVM) regression models. The second method
(DAFHEA II) is an enhancement on DAFHEA to accommodate for uncertain
environments. The third one uses surrogate ranking with preference learning or
ordinal regression. The fitness of the candidates is estimated by modeling
their rank. The techniques' performances on some of the benchmark numerical
optimization problems have been reported. The comparative benefits and
shortcomings of both techniques have been identified.
|
1303.2219 | The Vernam cipher is robust to small deviations from randomness | cs.CR cs.IT math.IT | The Vernam cipher (or one-time pad) has played an important rule in
cryptography because it is a perfect secrecy system. For example, if an English
text (presented in binary system) $X_1 X_2 ... $ is enciphered according to the
formula $Z_i = (X_i + Y_i) \mod 2 $, where $Y_1 Y_2 ...$ is a key sequence
generated by the Bernoulli source with equal probabilities of 0 and 1, anyone
who knows $Z_1 Z_2 ... $ has no information about $X_1 X_2 ... $ without the
knowledge of the key $Y_1 Y_2 ...$. (The best strategy is to guess $X_1 X_2 ...
$ not paying attention to $Z_1 Z_2 ... $.)
But what should one say about secrecy of an analogous method where the key
sequence $Y_1 Y_2 ...$ is generated by the Bernoulli source with a small bias,
say, $P(0) = 0.49, $ $ P(1) = 0.51$? To the best of our knowledge, there are no
theoretical estimates for the secrecy of such a system, as well as for the
general case where $X_1 X_2 ... $ (the plaintext) and key sequence are
described by stationary ergodic processes. We consider the running-key ciphers
where the plaintext and the key are generated by stationary ergodic sources and
show how to estimate the secrecy of such systems. In particular, it is shown
that, in a certain sense, the Vernam cipher is robust to small deviations from
randomness.
|
1303.2221 | Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann
Manifolds | cs.LG cs.CV cs.SI stat.ML | Relationships between entities in datasets are often of multiple nature, like
geographical distance, social relationships, or common interests among people
in a social network, for example. This information can naturally be modeled by
a set of weighted and undirected graphs that form a global multilayer graph,
where the common vertex set represents the entities and the edges on different
layers capture the similarities of the entities in term of the different
modalities. In this paper, we address the problem of analyzing multi-layer
graphs and propose methods for clustering the vertices by efficiently merging
the information provided by the multiple modalities. To this end, we propose to
combine the characteristics of individual graph layers using tools from
subspace analysis on a Grassmann manifold. The resulting combination can then
be viewed as a low dimensional representation of the original data which
preserves the most important information from diverse relationships between
entities. We use this information in new clustering methods and test our
algorithm on several synthetic and real world datasets where we demonstrate
superior or competitive performances compared to baseline and state-of-the-art
techniques. Our generic framework further extends to numerous analysis and
learning problems that involve different types of information on graphs.
|
1303.2223 | khmer: Working with Big Data in Bioinformatics | cs.CE q-bio.GN | We introduce design and optimization considerations for the 'khmer' package.
|
1303.2242 | Adaptive Network Dynamics and Evolution of Leadership in Collective
Migration | nlin.AO cs.SI physics.soc-ph q-bio.PE | The evolution of leadership in migratory populations depends not only on
costs and benefits of leadership investments but also on the opportunities for
individuals to rely on cues from others through social interactions. We derive
an analytically tractable adaptive dynamic network model of collective
migration with fast timescale migration dynamics and slow timescale adaptive
dynamics of individual leadership investment and social interaction. For large
populations, our analysis of bifurcations with respect to investment cost
explains the observed hysteretic effect associated with recovery of migration
in fragmented environments. Further, we show a minimum connectivity threshold
above which there is evolutionary branching into leader and follower
populations. For small populations, we show how the topology of the underlying
social interaction network influences the emergence and location of leaders in
the adaptive system. Our model and analysis can describe other adaptive network
dynamics involving collective tracking or collective learning of a noisy,
unknown signal, and likewise can inform the design of robotic networks where
agents use decentralized strategies that balance direct environmental
measurements with agent interactions.
|
1303.2251 | Zero-point attracting projection algorithm for sequential compressive
sensing | cs.IT math.IT | Sequential Compressive Sensing, which may be widely used in sensing devices,
is a popular topic of recent research. This paper proposes an online recovery
algorithm for sparse approximation of sequential compressive sensing. Several
techniques including warm start, fast iteration, and variable step size are
adopted in the proposed algorithm to improve its online performance. Finally,
numerical simulations demonstrate its better performance than the relative art.
|
1303.2255 | A Robust Zero-point Attraction LMS Algorithm on Near Sparse System
Identification | cs.IT math.IT | The newly proposed $l_1$ norm constraint zero-point attraction Least Mean
Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse
system identification. However, ZA-LMS has less advantage against standard LMS
when the system is near sparse. Thus, in this paper, firstly the near sparse
system modeling by Generalized Gaussian Distribution is recommended, where the
sparsity is defined accordingly. Secondly, two modifications to the ZA-LMS
algorithm have been made. The $l_1$ norm penalty is replaced by a partial $l_1$
norm in the cost function, enhancing robustness without increasing the
computational complexity. Moreover, the zero-point attraction item is weighted
by the magnitude of estimation error which adjusts the zero-point attraction
force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS
(DWZA-LMS) algorithm is further proposed, which shows better performance on
near sparse system identification. In addition, the mean square performance of
DWZA-LMS algorithm is analyzed. Finally, computer simulations demonstrate the
effectiveness of the proposed algorithm and verify the result of theoretical
analysis.
|
1303.2257 | A stochastic gradient approach on compressive sensing signal
reconstruction based on adaptive filtering framework | cs.IT math.IT | Based on the methodological similarity between sparse signal reconstruction
and system identification, a new approach for sparse signal reconstruction in
compressive sensing (CS) is proposed in this paper. This approach employs a
stochastic gradient-based adaptive filtering framework, which is commonly used
in system identification, to solve the sparse signal reconstruction problem.
Two typical algorithms for this problem: $l_0$-least mean square ($l_0$-LMS)
algorithm and $l_0$-exponentially forgetting window LMS ($l_0$-EFWLMS)
algorithm are hence introduced here. Both the algorithms utilize a zero
attraction method, which has been implemented by minimizing a continuous
approximation of $l_0$ norm of the studied signal. To improve the performances
of these proposed algorithms, an $l_0$-zero attraction projection ($l_0$-ZAP)
algorithm is also adopted, which has effectively accelerated their convergence
rates, making them much faster than the other existing algorithms for this
problem. Advantages of the proposed approach, such as its robustness against
noise etc., are demonstrated by numerical experiments.
|
1303.2261 | l_0 Norm Constraint LMS Algorithm for Sparse System Identification | cs.IT math.IT | In order to improve the performance of Least Mean Square (LMS) based system
identification of sparse systems, a new adaptive algorithm is proposed which
utilizes the sparsity property of such systems. A general approximating
approach on $l_0$ norm -- a typical metric of system sparsity, is proposed and
integrated into the cost function of the LMS algorithm. This integration is
equivalent to add a zero attractor in the iterations, by which the convergence
rate of small coefficients, that dominate the sparse system, can be effectively
improved. Moreover, using partial updating method, the computational complexity
is reduced. The simulations demonstrate that the proposed algorithm can
effectively improve the performance of LMS-based identification algorithms on
sparse system.
|
1303.2270 | Penalty-regulated dynamics and robust learning procedures in games | math.OC cs.GT cs.LG | Starting from a heuristic learning scheme for N-person games, we derive a new
class of continuous-time learning dynamics consisting of a replicator-like
drift adjusted by a penalty term that renders the boundary of the game's
strategy space repelling. These penalty-regulated dynamics are equivalent to
players keeping an exponentially discounted aggregate of their on-going payoffs
and then using a smooth best response to pick an action based on these
performance scores. Owing to this inherent duality, the proposed dynamics
satisfy a variant of the folk theorem of evolutionary game theory and they
converge to (arbitrarily precise) approximations of Nash equilibria in
potential games. Motivated by applications to traffic engineering, we exploit
this duality further to design a discrete-time, payoff-based learning algorithm
which retains these convergence properties and only requires players to observe
their in-game payoffs: moreover, the algorithm remains robust in the presence
of stochastic perturbations and observation errors, and it does not require any
synchronization between players.
|
1303.2277 | Is Learning to Rank Worth It? A Statistical Analysis of Learning to Rank
Methods | cs.IR | The Learning to Rank (L2R) research field has experienced a fast paced growth
over the last few years, with a wide variety of benchmark datasets and
baselines available for experimentation. We here investigate the main
assumption behind this field, which is that, the use of sophisticated L2R
algorithms and models, produce significant gains over more traditional and
simple information retrieval approaches. Our experimental results surprisingly
indicate that many L2R algorithms, when put up against the best individual
features of each dataset, may not produce statistically significant
differences, even if the absolute gains may seem large. We also find that most
of the reported baselines are statistically tied, with no clear winner.
|
1303.2280 | Stabilization of Networked Control Systems with Sparse
Observer-Controller Networks | math.OC cs.SY | In this paper we provide a set of stability conditions for linear
time-invariant networked control systems with arbitrary topology, using a
Lyapunov direct approach. We then use these stability conditions to provide a
novel low-complexity algorithm for the design of a sparse observer-based
control network. We employ distributed observers by employing the output of
other nodes to improve the stability of each observer dynamics. To avoid
unbounded growth of controller and observer gains, we impose bounds on their
norms. The effects of relaxation of these bounds is discussed when trying to
find the complete decentralization conditions.
|
1303.2284 | GenomeFingerprinter and universal genome fingerprint analysis for
systematic comparative genomics | q-bio.GN cs.CE math.NA | How to compare whole genome sequences at large scale has not been achieved
via conventional methods based on pair-wisely base-to-base comparison;
nevertheless, no attention was paid to handle in-one-sitting a number of
genomes crossing genetic category (chromosome, plasmid, and phage) with farther
divergences (much less or no homologous) over large size ranges (from Kbp to
Mbp). We created a new method, GenomeFingerprinter, to unambiguously produce
three-dimensional coordinates from a sequence, followed by one
three-dimensional plot and six two-dimensional trajectory projections to
illustrate whole genome fingerprints. We further developed a set of concepts
and tools and thereby established a new method, universal genome fingerprint
analysis. We demonstrated their applications through case studies on over a
hundred of genome sequences. Particularly, we defined the total genetic
component configuration (TGCC) (i.e., chromosome, plasmid, and phage) for
describing a strain as a system, and the universal genome fingerprint map
(UGFM) of TGCC for differentiating a strain as a universal system, as well as
the systematic comparative genomics (SCG) for comparing in-one-sitting a number
of genomes crossing genetic category in diverse strains. By using UGFM,
UGFM-TGCC, and UGFM-TGCC-SCG, we compared a number of genome sequences with
farther divergences (chromosome, plasmid, and phage; bacterium, archaeal
bacterium, and virus) over large size ranges (6Kbp~5Mbp), giving new insights
into critical problematic issues in microbial genomics in the post-genomic era.
This paper provided a new method for rapidly computing, geometrically
visualizing, and intuitively comparing genome sequences at fingerprint level,
and hence established a new method of universal genome fingerprint analysis for
systematic comparative genomics.
|
1303.2289 | Distributed optimization over time-varying directed graphs | math.OC cs.DC cs.SY | We consider distributed optimization by a collection of nodes, each having
access to its own convex function, whose collective goal is to minimize the sum
of the functions. The communications between nodes are described by a
time-varying sequence of directed graphs, which is uniformly strongly
connected. For such communications, assuming that every node knows its
out-degree, we develop a broadcast-based algorithm, termed the
subgradient-push, which steers every node to an optimal value under a standard
assumption of subgradient boundedness. The subgradient-push requires no
knowledge of either the number of agents or the graph sequence to implement.
Our analysis shows that the subgradient-push algorithm converges at a rate of
$O(\ln(t)/\sqrt{t})$, where the constant depends on the initial values at the
nodes, the subgradient norms, and, more interestingly, on both the consensus
speed and the imbalances of influence among the nodes.
|
1303.2292 | Intelligent Approaches to interact with Machines using Hand Gesture
Recognition in Natural way: A Survey | cs.HC cs.CV | Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient without wearing any extra device. HGR can be applied from games
control to vision enabled robot control, from virtual reality to smart home
systems. In this paper we are discussing work done in the area of hand gesture
recognition where focus is on the intelligent approaches including soft
computing based methods like artificial neural network, fuzzy logic, genetic
algorithms etc. The methods in the preprocessing of image for segmentation and
hand image construction also taken into study. Most researchers used fingertips
for hand detection in appearance based modeling. Finally the comparison of
results given by different researchers is also presented.
|
1303.2308 | Improving adaptation of ubiquitous recommander systems by using
reinforcement learning and collaborative filtering | cs.IR | The wide development of mobile applications provides a considerable amount of
data of all types (images, texts, sounds, videos, etc.). Thus, two main issues
have to be considered: assist users in finding information and reduce search
and navigation time. In this sense, context-based recommender systems (CBRS)
propose the user the adequate information depending on her/his situation. Our
work consists in applying machine learning techniques and reasoning process in
order to bring a solution to some of the problems concerning the acceptance of
recommender systems by users, namely avoiding the intervention of experts,
reducing cold start problem, speeding learning process and adapting to the
user's interest. To achieve this goal, we propose a fundamental modification in
terms of how we model the learning of the CBRS. Inspired by models of human
reasoning developed in robotic, we combine reinforcement learning and
case-based reasoning to define a contextual recommendation process based on
different context dimensions (cognitive, social, temporal, geographic). This
paper describes an ongoing work on the implementation of a CBRS based on a
hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative
filtering and case-based reasoning techniques. It also presents preliminary
results by comparing HyQL and the standard Q-Learning w.r.t. solving the cold
start problem.
|
1303.2309 | On the Performance Limits of Map-Aware Localization | cs.IT math.IT stat.AP | Establishing bounds on the accuracy achievable by localization techniques
represents a fundamental technical issue. Bounds on localization accuracy have
been derived for cases in which the position of an agent is estimated on the
basis of a set of observations and, possibly, of some a priori information
related to them (e.g., information about anchor positions and properties of the
communication channel). In this manuscript new bounds are derived under the
assumption that the localization system is map-aware, i.e., it can benefit not
only from the availability of observations, but also from the a priori
knowledge provided by the map of the environment where it operates. Our results
show that: a) map-aware estimation accuracy can be related to some features of
the map (e.g., its shape and area) even though, in general, the relation is
complicated; b) maps are really useful in the presence of some combination of
low signal-to-noise ratios and specific geometrical features of the map (e.g.,
the size of obstructions); c) in most cases, there is no need of refined maps
since additional details do not improve estimation accuracy.
|
1303.2310 | Trajectory Based Optimal Segment Computation in Road Network Databases | cs.DB | Finding a location for a new facility such that the facility attracts the
maximal number of customers is a challenging problem. Existing studies either
model customers as static sites and thus do not consider customer movement, or
they focus on theoretical aspects and do not provide solutions that are shown
empirically to be scalable. Given a road network, a set of existing facilities,
and a collection of customer route traversals, an optimal segment query returns
the optimal road network segment(s) for a new facility. We propose a practical
framework for computing this query, where each route traversal is assigned a
score that is distributed among the road segments covered by the route
according to a score distribution model. The query returns the road segment(s)
with the highest score. To achieve low latency, it is essential to prune the
very large search space. We propose two algorithms that adopt different
approaches to computing the query. Algorithm AUG uses graph augmentation, and
ITE uses iterative road-network partitioning. Empirical studies with real data
sets demonstrate that the algorithms are capable of offering high performance
in realistic settings.
|
1303.2314 | Mini-Batch Primal and Dual Methods for SVMs | cs.LG math.OC | We address the issue of using mini-batches in stochastic optimization of
SVMs. We show that the same quantity, the spectral norm of the data, controls
the parallelization speedup obtained for both primal stochastic subgradient
descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it
to derive novel variants of mini-batched SDCA. Our guarantees for both methods
are expressed in terms of the original nonsmooth primal problem based on the
hinge-loss.
|
1303.2330 | Image compression using anti-forensics method | cs.MM cs.CV | A large number of image forensics methods are available which are capable of
identifying image tampering. But these techniques are not capable of addressing
the anti-forensics method which is able to hide the trace of image tampering.
In this paper anti-forensics method for digital image compression has been
proposed. This anti-forensics method is capable of removing the traces of image
compression. Additionally, technique is also able to remove the traces of
blocking artifact that are left by image compression algorithms that divide an
image into segments during compression process. This method is targeted to
remove the compression fingerprints of JPEG compression.
|
1303.2364 | The Multidimensional Study of Viral Campaigns as Branching Processes | cs.SI physics.soc-ph | Viral campaigns on the Internet may follow variety of models, depending on
the content, incentives, personal attitudes of sender and recipient to the
content and other factors. Due to the fact that the knowledge of the campaign
specifics is essential for the campaign managers, researchers are constantly
evaluating models and real-world data. The goal of this article is to present
the new knowledge obtained from studying two viral campaigns that took place in
a virtual world which followed the branching process. The results show that it
is possible to reduce the time needed to estimate the model parameters of the
campaign and, moreover, some important aspects of time-generations relationship
are presented.
|
1303.2365 | Studying Paths of Participation in Viral Diffusion Process | cs.SI physics.soc-ph | Authors propose a conceptual model of participation in viral diffusion
process composed of four stages: awareness, infection, engagement and action.
To verify the model it has been applied and studied in the virtual social chat
environment settings. The study investigates the behavioral paths of actions
that reflect the stages of participation in the diffusion and presents
shortcuts, that lead to the final action, i.e. the attendance in a virtual
event. The results show that the participation in each stage of the process
increases the probability of reaching the final action. Nevertheless, the
majority of users involved in the virtual event did not go through each stage
of the process but followed the shortcuts. That suggests that the viral
diffusion process is not necessarily a linear sequence of human actions but
rather a dynamic system.
|
1303.2369 | Negative Effects of Incentivised Viral Campaigns for Activity in Social
Networks | cs.SI physics.soc-ph | Viral campaigns are crucial methods for word-of-mouth marketing in social
communities. The goal of these campaigns is to encourage people for activity.
The problem of incentivised and non-incentivised campaigns is studied in the
paper. Based on the data collected within the real social networking site both
approaches were compared. The experimental results revealed that a highly
motivated campaign not necessarily provides better results due to overlapping
effect. Additional studies have shown that the behaviour of individual
community members in the campaign based on their service profile can be
predicted but the classification accuracy may be limited.
|
1303.2379 | Conditions for Robustness of Polar Codes in the Presence of Channel
Mismatch | cs.IT math.IT | A challenging problem related to the design of polar codes is "robustness
against channel parameter variations" as stated in Ar{\i}kan's original work.
In this paper, we describe how the problem of robust polar code design can be
viewed as a mismatch decoding problem. We propose conditions which ensure a
polar encoder/decoder designed for a mismatched B-DMC can be used to
communicate reliably. In particular, the analysis shows that the original polar
code construction method is robust over the class of binary symmetric channels.
|
1303.2389 | Maximin Analysis of Message Passing Algorithms for Recovering Block
Sparse Signals | cs.IT math.IT | We consider the problem of recovering a block (or group) sparse signal from
an underdetermined set of random linear measurements, which appear in
compressed sensing applications such as radar and imaging. Recent results of
Donoho, Johnstone, and Montanari have shown that approximate message passing
(AMP) in combination with Stein's shrinkage outperforms group LASSO for large
block sizes. In this paper, we prove that, for a fixed block size and in the
strong undersampling regime (i.e., having very few measurements compared to the
ambient dimension), AMP cannot improve upon group LASSO, thereby complementing
the results of Donoho et al.
|
1303.2395 | State estimation under non-Gaussian Levy noise: A modified Kalman
filtering method | math.DS cs.IT cs.LG math.IT math.PR stat.ML | The Kalman filter is extensively used for state estimation for linear systems
under Gaussian noise. When non-Gaussian L\'evy noise is present, the
conventional Kalman filter may fail to be effective due to the fact that the
non-Gaussian L\'evy noise may have infinite variance. A modified Kalman filter
for linear systems with non-Gaussian L\'evy noise is devised. It works
effectively with reasonable computational cost. Simulation results are
presented to illustrate this non-Gaussian filtering method.
|
1303.2409 | Finite-time Stabilization of Circular Formations using Bearing-only
Measurements | cs.SY math.OC | This paper studies decentralized formation control of multiple vehicles when
each vehicle can only measure the local bearings of their neighbors by using
bearing-only sensors. Since the inter-vehicle distance cannot be measured, the
target formation involves no distance constraints. More specifically, the
target formation considered in this paper is an angle-constrained circular
formation, where each vehicle has exactly two neighbors and the angle at each
vehicle subtended by its two neighbors is pre-specified. To stabilize the
target formation, we propose a discontinuous control law that only requires the
sign information of the angle errors. Due to the discontinuity of the proposed
control law, the stability of the closed-loop system is analyzed by employing a
locally Lipschitz Lyapunov function and nonsmooth analysis tools. We prove that
the target formation is locally finite-time stable with collision avoidance
guaranteed. The evolution of the vehicle positions in the plane is also
characterized.
|
1303.2414 | A Bayesian Approach to Data Fusion in Sensor Networks | cs.IT math.IT | In this paper, we address the fusion problem in wireless sensor networks,
where the cross-correlation between the estimates is unknown. To solve the
problem within the Bayesian framework, we assume that the covariance matrix has
a prior distribution. We also assume that we know the covariance of each
estimate, i.e., the diagonal block of the entire covariance matrix (of the
random vector consisting of the two estimates). We then derive the conditional
distribution of the off-diagonal blocks, which is the cross-correlation of our
interest. We show that when there are two nodes, the conditional distribution
happens to be the inverted matrix variate $t$-distribution, from which we can
readily sample. For more than two nodes, the conditional distribution is no
longer the inverted matrix variate $t$-distribution. But we show that we can
decompose it into several sampling problems, each of which is the inverted
matrix variate $t$-distribution and therefore we can still sample from it.
Since we can sample from this distribution, it enables us to use the Monte
Carlo method to compute the minimum mean square error estimate for the fusion
problem. We use two models to generate experiment data and demonstrate the
generality of our method. Simulation results show that the proposed method
works better than the popular covariance intersection method.
|
1303.2417 | Linear NDCG and Pair-wise Loss | cs.LG stat.ML | Linear NDCG is used for measuring the performance of the Web content quality
assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove
that the DCG error equals a new pair-wise loss.
|
1303.2430 | Quantum and Concept Combination, Entangled Measurements and Prototype
Theory | cs.AI cs.CL quant-ph | We analyze the meaning of the violation of the marginal probability law for
situations of correlation measurements where entanglement is identified. We
show that for quantum theory applied to the cognitive realm such a violation
does not lead to the type of problems commonly believed to occur in situations
of quantum theory applied to the physical realm. We briefly situate our quantum
approach for modeling concepts and their combinations with respect to the
notions of 'extension' and 'intension' in theories of meaning, and in existing
concept theories.
|
1303.2437 | Least-Squares FIR Models of Low-Resolution MR data for Efficient
Phase-Error Compensation with Simultaneous Artefact Removal | cs.CV | Signal space models in both phase-encode, and frequency-encode directions are
presented for extrapolation of 2D partial kspace. Using the boxcar
representation of low-resolution spatial data, and a geometrical representation
of signal space vectors in both positive and negative phase-encode directions,
a robust predictor is constructed using a series of signal space projections.
Compared to some of the existing phase-correction methods that require
acquisition of a pre-determined set of fractional kspace lines, the proposed
predictor is found to be more efficient, due to its capability of exhibiting an
equivalent degree of performance using only half the number of fractional
lines. Robust filtering of noisy data is achieved using a second signal space
model in the frequency-encode direction, bypassing the requirement of a prior
highpass filtering operation. The signal space is constructed from Fourier
Transformed samples of each row in the low-resolution image. A set of FIR
filters are estimated by fitting a least squares model to this signal space.
Partial kspace extrapolation using the FIR filters is shown to result in
artifact-free reconstruction, particularly in respect of Gibbs ringing and
streaking type artifacts.
|
1303.2438 | A Taxonomy of Hyperlink Hiding Techniques | cs.IR | Hidden links are designed solely for search engines rather than visitors. To
get high search engine rankings, link hiding techniques are usually used for
the profitability of black industries, such as illicit game servers, false
medical services, illegal gambling, and less attractive high-profit industry,
etc. This paper investigates hyperlink hiding techniques on the Web, and gives
a detailed taxonomy. We believe the taxonomy can help develop appropriate
countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that
link hidden techniques are very prevalent on the Web. We also tried to explore
the attitude of Google towards link hiding spam by analyzing the PageRank
values of relative links. The results show that more should be done to punish
the hidden link spam.
|
1303.2439 | Voxel-wise Weighted MR Image Enhancement using an Extended Neighborhood
Filter | cs.CV | We present an edge preserving and denoising filter for enhancing the features
in images, which contain an ROI having a narrow spatial extent. Typical
examples include angiograms, or ROI spatially distributed in multiple locations
and contained within an outlying region, such as in multiple-sclerosis. The
filtering involves determination of multiplicative weights in the spatial
domain using an extended set of neighborhood directions. Equivalently, the
filtering operation may be interpreted as a combination of directional filters
in the frequency domain, with selective weighting for spatial frequencies
contained within each direction. The advantages of the proposed filter in
comparison to specialized non-linear filters, which operate on diffusion
principle, are illustrated using numerical phantom data. The performance
evaluation is carried out on simulated images from BrainWeb database for
multiple-sclerosis, acute ischemic stroke using clinically acquired FLAIR
images and MR angiograms.
|
1303.2446 | Broadening the Scope of Nanopublications | cs.DL cs.IR | In this paper, we present an approach for extending the existing concept of
nanopublications --- tiny entities of scientific results in RDF representation
--- to broaden their application range. The proposed extension uses English
sentences to represent informal and underspecified scientific claims. These
sentences follow a syntactic and semantic scheme that we call AIDA (Atomic,
Independent, Declarative, Absolute), which provides a uniform and succinct
representation of scientific assertions. Such AIDA nanopublications are
compatible with the existing nanopublication concept and enjoy most of its
advantages such as information sharing, interlinking of scientific findings,
and detailed attribution, while being more flexible and applicable to a much
wider range of scientific results. We show that users are able to create AIDA
sentences for given scientific results quickly and at high quality, and that it
is feasible to automatically extract and interlink AIDA nanopublications from
existing unstructured data sources. To demonstrate our approach, a web-based
interface is introduced, which also exemplifies the use of nanopublications for
non-scientific content, including meta-nanopublications that describe other
nanopublications.
|
1303.2448 | Automatic Detection of Non-deverbal Event Nouns for Quick Lexicon
Production | cs.CL | In this work we present the results of our experimental work on the
develop-ment of lexical class-based lexica by automatic means. The objective is
to as-sess the use of linguistic lexical-class based information as a feature
selection methodology for the use of classifiers in quick lexical development.
The results show that the approach can help in re-ducing the human effort
required in the development of language resources sig-nificantly.
|
1303.2449 | Using qualia information to identify lexical semantic classes in an
unsupervised clustering task | cs.CL | Acquiring lexical information is a complex problem, typically approached by
relying on a number of contexts to contribute information for classification.
One of the first issues to address in this domain is the determination of such
contexts. The work presented here proposes the use of automatically obtained
FORMAL role descriptors as features used to draw nouns from the same lexical
semantic class together in an unsupervised clustering task. We have dealt with
three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The
results obtained show that it is possible to discriminate between elements from
different lexical semantic classes using only FORMAL role information, hence
validating our initial hypothesis. Also, iterating our method accurately
accounts for fine-grained distinctions within lexical classes, namely
distinctions involving ambiguous expressions. Moreover, a filtering and
bootstrapping strategy employed in extracting FORMAL role descriptors proved to
minimize effects of sparse data and noise in our task.
|
1303.2465 | A Low-Complexity Algorithm for Static Background Estimation from
Cluttered Image Sequences in Surveillance Contexts | cs.CV | For the purposes of foreground estimation, the true background model is
unavailable in many practical circumstances and needs to be estimated from
cluttered image sequences. We propose a sequential technique for static
background estimation in such conditions, with low computational and memory
requirements. Image sequences are analysed on a block-by-block basis. For each
block location a representative set is maintained which contains distinct
blocks obtained along its temporal line. The background estimation is carried
out in a Markov Random Field framework, where the optimal labelling solution is
computed using iterated conditional modes. The clique potentials are computed
based on the combined frequency response of the candidate block and its
neighbourhood. It is assumed that the most appropriate block results in the
smoothest response, indirectly enforcing the spatial continuity of structures
within a scene. Experiments on real-life surveillance videos demonstrate that
the proposed method obtains considerably better background estimates (both
qualitatively and quantitatively) than median filtering and the recently
proposed "intervals of stable intensity" method. Further experiments on the
Wallflower dataset suggest that the combination of the proposed method with a
foreground segmentation algorithm results in improved foreground segmentation.
|
1303.2506 | Monte-Carlo utility estimates for Bayesian reinforcement learning | cs.LG stat.ML | This paper introduces a set of algorithms for Monte-Carlo Bayesian
reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the
Bayes-optimal value function is employed to construct an optimistic policy.
Secondly, gradient-based algorithms for approximate upper and lower bounds are
introduced. Finally, we introduce a new class of gradient algorithms for
Bayesian Bellman error minimisation. We theoretically show that the gradient
methods are sound. Experimentally, we demonstrate the superiority of the upper
bound method in terms of reward obtained. However, we also show that the
Bayesian Bellman error method is a close second, despite its significant
computational simplicity.
|
1303.2542 | Robust Smoothing for Estimating Optical Phase Varying as a Continuous
Resonant Process | math.OC cs.SY quant-ph | Continuous phase estimation is known to be superior in accuracy as compared
to static estimation. The estimation process is, however, desired to be made
robust to uncertainties in the underlying parameters. Here, homodyne phase
estimation of coherent and squeezed states of light, evolving continuously
under the influence of a second-order resonant noise process, are made robust
to parameter uncertainties using a robust fixed-interval smoother, designed for
uncertain systems satisfying a certain integral quadratic constraint. We
observe that such a robust smoother provides improved worst-case performance
over the optimal smoother and also performs better than a robust filter for the
uncertain system.
|
1303.2545 | Optimization of the parity-check matrix density in QC-LDPC code-based
McEliece cryptosystems | cs.IT cs.CR math.IT | Low-density parity-check (LDPC) codes are one of the most promising families
of codes to replace the Goppa codes originally used in the McEliece
cryptosystem. In fact, it has been shown that by using quasi-cyclic low-density
parity-check (QC-LDPC) codes in this system, drastic reductions in the public
key size can be achieved, while maintaining fixed security levels. Recently,
some proposals have appeared in the literature using codes with denser
parity-check matrices, named moderate-density parity-check (MDPC) codes.
However, the density of the parity-check matrices to be used in QC-LDPC
code-based variants of the McEliece cryptosystem has never been optimized. This
paper aims at filling such gap, by proposing a procedure for selecting the
density of the private parity-check matrix, based on the security level and the
decryption complexity. We provide some examples of the system parameters
obtained through the proposed technique.
|
1303.2547 | On a family of binary completely transitive codes with growing covering
radius | cs.IT math.IT | A new family of binary linear completely transitive (and, therefore,
completely regular) codes is constructed. The covering radius of these codes is
growing with the length of the code. In particular, for any integer r > 1,
there exist two codes with d=3, covering radius r and length 2r(4r-1) and
(2r+1)(4r+1), respectively. These new completely transitive codes induce, as
coset graphs, a family of distance-transitive graphs of growing diameter.
|
1303.2579 | One-shot source coding with coded side information available at the
decoder | cs.IT math.IT | One-shot achievable rate region for source coding when coded side information
is available at the decoder (source coding with a helper) is proposed. The
achievable region proposed is in terms of conditional smooth max Renyi entropy
and smooth max Renyi divergence. Asymptotically (in the limit of large block
lengths) this region is quantified in terms of spectral-sup conditional entropy
rate and spectral- sup mutual information rate. In particular, it coincides
with the rate region derived in the limit of unlimited independent and
identically distributed copies of the sources.
|
1303.2587 | Multicell Random Beamforming with CDF-based Scheduling: Exact Rate and
Scaling Laws | cs.IT math.IT | In a multicell multiuser MIMO downlink employing random beamforming as the
transmission scheme, the heterogeneous large scale channel effects of intercell
and intracell interference complicate analysis of distributed scheduling based
systems. In this paper, we extend the analysis in [1] and [2] to study the
aforementioned challenging scenario. The cumulative distribution function
(CDF)-based scheduling policy utilized in [1] and [2] is leveraged to maintain
fairness among users and simultaneously obtain multiuser diversity gain. The
closed form expression of the individual sum rate for each user is derived
under the CDF-based scheduling policy. More importantly, with this distributed
scheduling policy, we conduct asymptotic (in users) analysis to determine the
limiting distribution of the signal-to-interference-plus-noise ratio, and
establish the individual scaling laws for each user.
|
1303.2595 | Integrating Space, Time, Version and Scale Using Alexandrov Topologies | cs.DB | This article introduces a novel approach to spatial database design. Instead
of extending the canonical Solid-Face-Edge-Vertex schema by, say, "hypersolids"
these classes are generalised to a common type SpatialEntity, and the
individual BoundedBy relations between two consecutive classes are generalised
to one BoundedBy relation on SpatialEntity instances. Then the pair
(SpatialEntity, BoundedBy) is a so-called incidence graph.
The novelty about this approach uses the observation that an incidence graph
represents a topological space of SpatialEntity instances because the
BoundedBy-relation defines a so-called Alexandrov topology for them turning
them into a topological space. So spatial data becomes part of mathematical
topology and topology can be immediately applied to spatial data. For example,
continuous functions between two instances of spatial data allow the consistent
modelling of generalisation. Further, it is also possible to establish a formal
topological definition of spatial data dimension, and every topological data
model of arbitrary dimension gets a simple uniform data model. This model
covers space-time, and the version history of a spatial model can be
represented by an Alexandrov topology, too. By integrating space, time,
version, and scale into one single schema, topological queries across those
aspects are enabled through topological constructions. In fact, the topological
constructions cover a relationally complete query language for spaces and can
be redefined to operate accordingly on their graph representations.
With these observations a relational database schema for a spatial data model
of dimension 6 and more is developed. The schema seamlessly integrates 4D
space-time, levels of detail and version history, and it can be easily expanded
to also contain non-spatial information or be linked to other data sources.
|
1303.2607 | Joint optimization of fitting & matching in multi-view reconstruction | cs.CV | Many standard approaches for geometric model fitting are based on pre-matched
image features. Typically, such pre-matching uses only feature appearances
(e.g. SIFT) and a large number of non-unique features must be discarded in
order to control the false positive rate. In contrast, we solve feature
matching and multi-model fitting problems in a joint optimization framework.
This paper proposes several fit-&-match energy formulations based on a
generalization of the assignment problem. We developed an efficient solver
based on min-cost-max-flow algorithm that finds near optimal solutions. Our
approach significantly increases the number of detected matches. In practice,
energy-based joint fitting & matching allows to increase the distance between
view-points previously restricted by robustness of local SIFT-matching and to
improve the model fitting accuracy when compared to state-of-the-art
multi-model fitting techniques.
|
1303.2610 | Kernel Sparse Models for Automated Tumor Segmentation | cs.CV | In this paper, we propose sparse coding-based approaches for segmentation of
tumor regions from MR images. Sparse coding with data-adapted dictionaries has
been successfully employed in several image recovery and vision problems. The
proposed approaches obtain sparse codes for each pixel in brain magnetic
resonance images considering their intensity values and location information.
Since it is trivial to obtain pixel-wise sparse codes, and combining multiple
features in the sparse coding setup is not straightforward, we propose to
perform sparse coding in a high-dimensional feature space where non-linear
similarities can be effectively modeled. We use the training data from
expert-segmented images to obtain kernel dictionaries with the kernel K-lines
clustering procedure. For a test image, sparse codes are computed with these
kernel dictionaries, and they are used to identify the tumor regions. This
approach is completely automated, and does not require user intervention to
initialize the tumor regions in a test image. Furthermore, a low complexity
segmentation approach based on kernel sparse codes, which allows the user to
initialize the tumor region, is also presented. Results obtained with both the
proposed approaches are validated against manual segmentation by an expert
radiologist, and the proposed methods lead to accurate tumor identification.
|
1303.2631 | Quantum filtering using POVM measurements | quant-ph cs.SY math.PR | The objective of this work is to develop a recursive, discrete time quantum
filtering equation for a system that interacts with a probe, on which
measurements are performed according to the Positive Operator Valued Measures
(POVMs) framework. POVMs are the most general measurements one can make on a
quantum system and although in principle they can be reformulated as projective
measurements on larger spaces, for which filtering results exist, a direct
treatment of POVMs is more natural and can simplify the filter computations for
some applications. Hence we formalize the notion of strongly commuting (Davies)
instruments which allows one to develop joint measurement statistics for POVM
type measurements. This allows us to prove the existence of conditional POVMs,
which is essential for the development of a filtering equation. We demonstrate
that under generally satisfied assumptions, knowing the observed probe POVM
operator is sufficient to uniquely specify the quantum filtering evolution for
the system.
|
1303.2636 | Energy Cooperation in Energy Harvesting Communications | cs.IT cs.NI math.IT | In energy harvesting communications, users transmit messages using energy
harvested from nature during the course of communication. With an optimum
transmit policy, the performance of the system depends only on the energy
arrival profiles. In this paper, we introduce the concept of energy
cooperation, where a user wirelessly transmits a portion of its energy to
another energy harvesting user. This enables shaping and optimization of the
energy arrivals at the energy-receiving node, and improves the overall system
performance, despite the loss incurred in energy transfer. We consider several
basic multi-user network structures with energy harvesting and wireless energy
transfer capabilities: relay channel, two-way channel and multiple access
channel. We determine energy management policies that maximize the system
throughput within a given duration using a Lagrangian formulation and the
resulting KKT optimality conditions. We develop a two-dimensional directional
water-filling algorithm which optimally controls the flow of harvested energy
in two dimensions: in time (from past to future) and among users (from
energy-transferring to energy-receiving) and show that a generalized version of
this algorithm achieves the boundary of the capacity region of the two-way
channel.
|
1303.2643 | Revealing Cluster Structure of Graph by Path Following Replicator
Dynamic | cs.LG cs.GT | In this paper, we propose a path following replicator dynamic, and
investigate its potentials in uncovering the underlying cluster structure of a
graph. The proposed dynamic is a generalization of the discrete replicator
dynamic. The replicator dynamic has been successfully used to extract dense
clusters of graphs; however, it is often sensitive to the degree distribution
of a graph, and usually biased by vertices with large degrees, thus may fail to
detect the densest cluster. To overcome this problem, we introduce a dynamic
parameter, called path parameter, into the evolution process. The path
parameter can be interpreted as the maximal possible probability of a current
cluster containing a vertex, and it monotonically increases as evolution
process proceeds. By limiting the maximal probability, the phenomenon of some
vertices dominating the early stage of evolution process is suppressed, thus
making evolution process more robust. To solve the optimization problem with a
fixed path parameter, we propose an efficient fixed point algorithm. The time
complexity of the path following replicator dynamic is only linear in the
number of edges of a graph, thus it can analyze graphs with millions of
vertices and tens of millions of edges on a common PC in a few minutes.
Besides, it can be naturally generalized to hypergraph and graph with edges of
different orders. We apply it to four important problems: maximum clique
problem, densest k-subgraph problem, structure fitting, and discovery of
high-density regions. The extensive experimental results clearly demonstrate
its advantages, in terms of robustness, scalability and flexility.
|
1303.2651 | Hybrid Q-Learning Applied to Ubiquitous recommender system | cs.LG cs.IR | Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to bring a solution to some of the
problems concerning the acceptance of the system by users. To achieve this, we
propose a fundamental shift in terms of how we model the learning of
recommender system: inspired by models of human reasoning developed in robotic,
we combine reinforcement learning and case-base reasoning to define a
recommendation process that uses these two approaches for generating
recommendations on different context dimensions (social, temporal, geographic).
We describe an implementation of the recommender system based on this
framework. We also present preliminary results from experiments with the system
and show how our approach increases the recommendation quality.
|
1303.2663 | Spectral Clustering with Epidemic Diffusion | cs.SI cs.LG physics.soc-ph stat.ML | Spectral clustering is widely used to partition graphs into distinct modules
or communities. Existing methods for spectral clustering use the eigenvalues
and eigenvectors of the graph Laplacian, an operator that is closely associated
with random walks on graphs. We propose a new spectral partitioning method that
exploits the properties of epidemic diffusion. An epidemic is a dynamic process
that, unlike the random walk, simultaneously transitions to all the neighbors
of a given node. We show that the replicator, an operator describing epidemic
diffusion, is equivalent to the symmetric normalized Laplacian of a reweighted
graph with edges reweighted by the eigenvector centralities of their incident
nodes. Thus, more weight is given to edges connecting more central nodes. We
describe a method that partitions the nodes based on the componentwise ratio of
the replicator's second eigenvector to the first, and compare its performance
to traditional spectral clustering techniques on synthetic graphs with known
community structure. We demonstrate that the replicator gives preference to
dense, clique-like structures, enabling it to more effectively discover
communities that may be obscured by dense intercommunity linking.
|
1303.2685 | Bilateral Filter: Graph Spectral Interpretation and Extensions | cs.CV | In this paper we study the bilateral filter proposed by Tomasi and Manduchi,
as a spectral domain transform defined on a weighted graph. The nodes of this
graph represent the pixels in the image and a graph signal defined on the nodes
represents the intensity values. Edge weights in the graph correspond to the
bilateral filter coefficients and hence are data adaptive. Spectrum of a graph
is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian
matrix. We use this spectral interpretation to generalize the bilateral filter
and propose more flexible and application specific spectral designs of
bilateral-like filters. We show that these spectral filters can be implemented
with k-iterative bilateral filtering operations and do not require expensive
diagonalization of the Laplacian matrix.
|
1303.2709 | Resilient Continuous-Time Consensus in Fractional Robust Networks | cs.SY | In this paper, we study the continuous-time consensus problem in the presence
of adversaries. The networked multi-agent system is modeled as a switched
system, where the normal agents have integrator dynamics and the switching
signal determines the topology of the network. We consider several models of
omniscient adversaries under the assumption that at most a fraction of any
normal agent's neighbors may be adversaries. Under this fractional assumption
on the interaction between normal and adversary agents, we show that a novel
graph theoretic metric, called fractional robustness, is useful for analyzing
the network topologies under which the normal agents achieve consensus.
|
1303.2720 | Low-Complexity Constrained Constant Modulus SG-based Beamforming
Algorithms with Variable Step Size | cs.IT math.IT | In this paper, two low-complexity adaptive step size algorithms are
investigated for blind adaptive beamforming. Both of them are used in a
stochastic gradient (SG) algorithm, which employs the constrained constant
modulus (CCM) criterion as the design approach. A brief analysis is given for
illustrating their properties. Simulations are performed to compare the
performances of the novel algorithms with other well-known methods. Results
indicate that the proposed algorithms achieve superior performance, better
convergence behavior and lower computational complexity in both stationary and
non-stationary environments.
|
1303.2721 | Guaranteed Performance Leader-follower Control for Multi-agent Systems
with Linear IQC-Constrained Coupling | cs.SY | This paper considers the leader-follower control problem for a linear
multi-agent system with undirected topology and linear coupling subject to
integral quadratic constraints (IQCs). A consensus-type control protocol is
proposed based on each agent's states relative its neighbors. In addition a
selected set of agents uses for control their states relative the leader. Using
a coordinate transformation, the consensus analysis of the multi-agent system
is recast as a decentralized robust control problem for an auxiliary
interconnected large scale system. Based on this interconnected large scale
system, sufficient conditions are obtained which guarantee that the system
tracks the leader. These conditions guarantee a suboptimal bound on the system
tracking performance. The effectiveness of the proposed method is demonstrated
using a simulation example.
|
1303.2725 | Robust blind methods using $\ell_p$ quasi norms | cs.IT math.IT | It was shown in a previous work that some blind methods can be made robust to
channel order overmodeling by using the $\ell_1$ or $\ell_p$ quasi-norms.
However, no theoretical argument has been provided to support this statement.
In this work, we study the robustness of subspace blind based methods using
$\ell_1$ or $\ell_p$ quasi-norms. For the $\ell_1$ norm, we provide the
sufficient and necessary condition that the channel should satisfy in order to
ensure its identifiability in the noise-less case. We then study its frequency
of occurrence, and deduce the effect of channel parameters on the robustness of
blind subspace methods using $\ell_1$ norms.
|
1303.2735 | Efficient Codes for Limited View Adversarial Channels | cs.IT math.IT | We introduce randomized Limited View (LV) adversary codes that provide
protection against an adversary that uses their partial view of the
communication to construct an adversarial error vector to be added to the
channel. For a codeword of length N, the adversary selects a subset of \rho_rN
of the codeword components to "see", and then "adds" an adversarial error
vector of weight \rho_wN to the codeword. Performance of the code is measured
by the probability of the decoder failure in recovering the sent message. An
(N, q^{RN},\delta)-limited view adversary code ensures that the success chance
of the adversary in making decoder fail, is bounded by \delta when the
information rate of the code is at least R. Our main motivation to study these
codes is providing protection for wireless communication at the physical layer
of networks.
We formalize the definition of adversarial error and decoder failure,
construct a code with efficient encoding and decoding that allows the adversary
to, depending on the code rate, read up to half of the sent codeword and add
error on the same coordinates. The code is non-linear, has an efficient
decoding algorithm, and is constructed using a message authentication code
(MAC) and a Folded Reed-Solomon (FRS) code. The decoding algorithm uses an
innovative approach that combines the list decoding algorithm of the FRS codes
and the MAC verification algorithm to eliminate the exponential size of the
list output from the decoding algorithm. We discuss application of our results
to Reliable Message Transmission problem, and open problems for future work.
|
1303.2739 | Machine Learning for Bioclimatic Modelling | cs.LG stat.AP | Many machine learning (ML) approaches are widely used to generate bioclimatic
models for prediction of geographic range of organism as a function of climate.
Applications such as prediction of range shift in organism, range of invasive
species influenced by climate change are important parameters in understanding
the impact of climate change. However, success of machine learning-based
approaches depends on a number of factors. While it can be safely said that no
particular ML technique can be effective in all applications and success of a
technique is predominantly dependent on the application or the type of the
problem, it is useful to understand their behavior to ensure informed choice of
techniques. This paper presents a comprehensive review of machine
learning-based bioclimatic model generation and analyses the factors
influencing success of such models. Considering the wide use of statistical
techniques, in our discussion we also include conventional statistical
techniques used in bioclimatic modelling.
|
1303.2745 | Evolutionary Approaches to Expensive Optimisation | cs.NE | Surrogate assisted evolutionary algorithms (EA) are rapidly gaining
popularity where applications of EA in complex real world problem domains are
concerned. Although EAs are powerful global optimizers, finding optimal
solution to complex high dimensional, multimodal problems often require very
expensive fitness function evaluations. Needless to say, this could brand any
population-based iterative optimization technique to be the most crippling
choice to handle such problems. Use of approximate model or surrogates provides
a much cheaper option. However, naturally this cheaper option comes with its
own price. This paper discusses some of the key issues involved with use of
approximation in evolutionary algorithm, possible best practices and solutions.
Answers to the following questions have been sought: what type of fitness
approximation to be used; which approximation model to use; how to integrate
the approximation model in EA; how much approximation to use; and how to ensure
reliable approximation.
|
1303.2751 | Gaussian Mixture Model for Handwritten Script Identification | cs.CV | This paper presents a Gaussian Mixture Model (GMM) to identify the script of
handwritten words of Roman, Devanagari, Kannada and Telugu scripts. It
emphasizes the significance of directional energies for identification of
script of the word. It is robust to varied image sizes and different styles of
writing. A GMM is modeled using a set of six novel features derived from
directional energy distributions of the underlying image. The standard
deviation of directional energy distributions are computed by decomposing an
image matrix into right and left diagonals. Furthermore, deviation of
horizontal and vertical distributions of energies is also built-in to GMM. A
dataset of 400 images out of 800 (200 of each script) are used for training GMM
and the remaining is for testing. An exhaustive experimentation is carried out
at bi-script, tri-script and multi-script level and achieved script
identification accuracies in percentage as 98.7, 98.16 and 96.91 respectively.
|
1303.2766 | Optimized Transmission with Improper Gaussian Signaling in the K-User
MISO Interference Channel | cs.IT math.IT | This paper studies the achievable rate region of the K-user Gaussian
multiple-input single-output interference channel (MISO-IC) with the
interference treated as noise, when improper or circularly asymmetric complex
Gaussian signaling is applied. The transmit optimization with improper Gaussian
signaling involves not only the signal covariance matrix as in the conventional
proper or circularly symmetric Gaussian signaling, but also the signal
pseudo-covariance matrix, which is conventionally set to zero in proper
Gaussian signaling. By exploiting the separable rate expression with improper
Gaussian signaling, we propose a separate transmit covariance and
pseudo-covariance optimization algorithm, which is guaranteed to improve the
users' achievable rates over the conventional proper Gaussian signaling. In
particular, for the pseudo-covariance optimization, we establish the optimality
of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit
covariance matrices for achieving the Pareto boundary of the rate region. Based
on this result, we are able to greatly reduce the number of variables in the
pseudo-covariance optimization problem and thereby develop an efficient
solution by applying the celebrated semidefinite relaxation (SDR) technique.
Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC)
with improper Gaussian signaling or so-called widely linear transmit precoding.
|
1303.2774 | Joint Beamforming and Power Control in Coordinated Multicell: Max-Min
Duality, Effective Network and Large System Transition | cs.IT math.IT | This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.
|
1303.2783 | Combined Learning of Salient Local Descriptors and Distance Metrics for
Image Set Face Verification | cs.CV | In contrast to comparing faces via single exemplars, matching sets of face
images increases robustness and discrimination performance. Recent image set
matching approaches typically measure similarities between subspaces or
manifolds, while representing faces in a rigid and holistic manner. Such
representations are easily affected by variations in terms of alignment,
illumination, pose and expression. While local feature based representations
are considerably more robust to such variations, they have received little
attention within the image set matching area. We propose a novel image set
matching technique, comprised of three aspects: (i) robust descriptors of face
regions based on local features, partly inspired by the hierarchy in the human
visual system, (ii) use of several subspace and exemplar metrics to compare
corresponding face regions, (iii) jointly learning which regions are the most
discriminative while finding the optimal mixing weights for combining metrics.
Face recognition experiments on LFW, PIE and MOBIO face datasets show that the
proposed algorithm obtains considerably better performance than several recent
state-of-the-art techniques, such as Local Principal Angle and the Kernel
Affine Hull Method.
|
1303.2789 | A Cooperative Q-learning Approach for Real-time Power Allocation in
Femtocell Networks | cs.MA cs.LG | In this paper, we address the problem of distributed interference management
of cognitive femtocells that share the same frequency range with macrocells
(primary user) using distributed multi-agent Q-learning. We formulate and solve
three problems representing three different Q-learning algorithms: namely,
centralized, distributed and partially distributed power control using
Q-learning (CPC-Q, DPC-Q and PDPC-Q). CPCQ, although not of practical interest,
characterizes the global optimum. Each of DPC-Q and PDPC-Q works in two
different learning paradigms: Independent (IL) and Cooperative (CL). The former
is considered the simplest form for applying Qlearning in multi-agent
scenarios, where all the femtocells learn independently. The latter is the
proposed scheme in which femtocells share partial information during the
learning process in order to strike a balance between practical relevance and
performance. In terms of performance, the simulation results showed that the CL
paradigm outperforms the IL paradigm and achieves an aggregate femtocells
capacity that is very close to the optimal one. For the practical relevance
issue, we evaluate the robustness and scalability of DPC-Q, in real time, by
deploying new femtocells in the system during the learning process, where we
showed that DPC-Q in the CL paradigm is scalable to large number of femtocells
and more robust to the network dynamics compared to the IL paradigm
|
1303.2792 | Modeling Basic Aspects of Cyber-Physical Systems | cs.RO | Designing novel cyber-physical systems entails significant, costly physical
experimentation. Simulation tools can enable the virtualization of experiments.
Unfortunately, current tools have shortcomings that limit their utility for
virtual experimentation. Language research can be especially helpful in
addressing many of these problems. As a first step in this direction, we
consider the question of determining what language features are needed to model
cyber-physical systems. Using a series of elementary examples of cyber-physical
systems, we reflect on the extent to which a small, experimental
domain-specific formalism called Acumen suffices for this purpose.
|
1303.2799 | Spectral Compressive Sensing with Polar Interpolation | cs.IT math.IT | Existing approaches to compressive sensing of frequency-sparse signals
focuses on signal recovery rather than spectral estimation. Furthermore, the
recovery performance is limited by the coherence of the required sparsity
dictionaries and by the discretization of the frequency parameter space. In
this paper, we introduce a greedy recovery algorithm that leverages a
band-exclusion function and a polar interpolation function to address these two
issues in spectral compressive sensing. Our algorithm is geared towards line
spectral estimation from compressive measurements and outperforms most existing
approaches in fidelity and tolerance to noise.
|
1303.2812 | Energy-Efficient Power Control for Contention-Based Synchronization in
OFDMA Systems with Discrete Powers and Limited Feedback | cs.IT math.IT | This work derives a distributed and iterative algorithm by which mobile
terminals can selfishly control their transmit powers during the
synchronization procedure specified by the IEEE 802.16m and the 3GPP-LTE
standards for orthogonal frequency-division multiple-access technologies. The
proposed solution aims at maximizing the energy efficiency of the network and
is derived on the basis of a finite noncooperative game in which the players
have discrete action sets of transmit powers. The set of Nash equilibria of the
game is investigated, and a distributed power control algorithm is proposed to
achieve synchronization in an energy-efficient manner under the assumption that
the feedback from the base station is limited. Numerical results show that the
proposed solution improves the energy efficiency as well as the timing
estimation accuracy of the network compared to existing alternatives, while
requiring a reasonable amount of information to be exchanged on the return
channel.
|
1303.2817 | A Tutorial on the Optimization of Amplify-and-Forward MIMO Relay Systems | cs.IT math.IT | The remarkable promise of multiple-input multiple-output (MIMO) wireless
channels has motivated an intense research activity to characterize the
theoretical and practical issues associated with the design of transmit
(source) and receive (destination) processing matrices under different
operating conditions. This activity was primarily focused on point-to-point
(single-hop) communications but more recently there has been an extensive work
on two-hop or multi-hop settings in which single or multiple relays are used to
deliver the information from the source to the destination. The aim of this
tutorial is to provide an up-to-date overview of the fundamental results and
practical implementation issues of designing amplify-and-forward MIMO relay
systems.
|
1303.2820 | Power Allocation in Two-Hop Amplify-and-Forward MIMO Relay Systems with
QoS requirements | cs.IT math.IT | The problem of minimizing the total power consumption while satisfying
different quality-of-service (QoS) requirements in a two-hop multiple-input
multiple-output network with a single non-regenerative relay is considered. As
shown by Y. Rong in [1], the optimal processing matrices for both linear and
non-linear transceiver architectures lead to the diagonalization of the
source-relay-destination channel so that the power minimization problem reduces
to properly allocating the available power over the established links.
Unfortunately, finding the solution of this problem is numerically difficult as
it is not in a convex form. To overcome this difficulty, existing solutions
rely on the computation of upper- and lower-bounds that are hard to obtain or
require the relaxation of the QoS constraints. In this work, a novel approach
is devised for both linear and non-linear transceiver architectures, which
allows to closely approximate the solutions of the non-convex power allocation
problems with those of convex ones easy to compute in closed-form by means of
multi-step procedures of reduced complexity. Computer simulations are used to
assess the performance of the proposed approach and to make comparisons with
alternatives.
|
1303.2823 | Gaussian Processes for Nonlinear Signal Processing | cs.LG cs.IT math.IT stat.ML | Gaussian processes (GPs) are versatile tools that have been successfully
employed to solve nonlinear estimation problems in machine learning, but that
are rarely used in signal processing. In this tutorial, we present GPs for
regression as a natural nonlinear extension to optimal Wiener filtering. After
establishing their basic formulation, we discuss several important aspects and
extensions, including recursive and adaptive algorithms for dealing with
non-stationarity, low-complexity solutions, non-Gaussian noise models and
classification scenarios. Furthermore, we provide a selection of relevant
applications to wireless digital communications.
|
1303.2826 | Probabilistic Topic and Syntax Modeling with Part-of-Speech LDA | cs.CL | This article presents a probabilistic generative model for text based on
semantic topics and syntactic classes called Part-of-Speech LDA (POSLDA).
POSLDA simultaneously uncovers short-range syntactic patterns (syntax) and
long-range semantic patterns (topics) that exist in document collections. This
results in word distributions that are specific to both topics (sports,
education, ...) and parts-of-speech (nouns, verbs, ...). For example,
multinomial distributions over words are uncovered that can be understood as
"nouns about weather" or "verbs about law". We describe the model and an
approximate inference algorithm and then demonstrate the quality of the learned
topics both qualitatively and quantitatively. Then, we discuss an NLP
application where the output of POSLDA can lead to strong improvements in
quality: unsupervised part-of-speech tagging. We describe algorithms for this
task that make use of POSLDA-learned distributions that result in improved
performance beyond the state of the art.
|
1303.2830 | Almost sure convergence of a randomized algorithm for relative
localization in sensor networks | cs.SY math.OC | This paper regards the relative localization problem in sensor networks. We
study a randomized algorithm, which is based on input-driven consensus dynamics
and involves pairwise "gossip" communications and updates. Due to the
randomness of the updates, the state of this algorithm ergodically oscillates
around a limit value. Exploiting the ergodicity of the dynamics, we show that
the time-average of the state almost surely converges to the least-squares
solution of the localization problem. Remarkably, the computation of the
time-average does not require the sensors to share any common clock. Hence, the
proposed algorithm is fully distributed and asynchronous.
|
1303.2844 | A Stochastic Grammar for Natural Shapes | cs.CV | We consider object detection using a generic model for natural shapes. A
common approach for object recognition involves matching object models directly
to images. Another approach involves building intermediate representations via
a generic grouping processes. We argue that these two processes (model-based
recognition and grouping) may use similar computational mechanisms. By defining
a generic model for shapes we can use model-based techniques to implement a
mid-level vision grouping process.
|
1303.2860 | Fairness in Academic Course Timetabling | cs.AI cs.DS | We consider the problem of creating fair course timetables in the setting of
a university. Our motivation is to improve the overall satisfaction of
individuals concerned (students, teachers, etc.) by providing a fair timetable
to them. The central idea is that undesirable arrangements in the course
timetable, i.e., violations of soft constraints, should be distributed in a
fair way among the individuals. We propose two formulations for the fair course
timetabling problem that are based on max-min fairness and Jain's fairness
index, respectively. Furthermore, we present and experimentally evaluate an
optimization algorithm based on simulated annealing for solving max-min fair
course timetabling problems. The new contribution is concerned with measuring
the energy difference between two timetables, i.e., how much worse a timetable
is compared to another timetable with respect to max-min fairness. We introduce
three different energy difference measures and evaluate their impact on the
overall algorithm performance. The second proposed problem formulation focuses
on the tradeoff between fairness and the total amount of soft constraint
violations. Our experimental evaluation shows that the known best solutions to
the ITC2007 curriculum-based course timetabling instances are quite fair with
respect to Jain's fairness index. However, the experiments also show that the
fairness can be improved further for only a rather small increase in the total
amount of soft constraint violations.
|
1303.2870 | CoMP Meets Smart Grid: A New Communication and Energy Cooperation
Paradigm | cs.IT math.IT | In this paper, we pursue a unified study on smart grid and coordinated
multi-point (CoMP) enabled wireless communication by investigating a new joint
communication and energy cooperation approach. We consider a practical CoMP
system with clustered multiple-antenna base stations (BSs) cooperatively
communicating with multiple single-antenna mobile terminals (MTs), where each
BS is equipped with local renewable energy generators to supply power and also
a smart meter to enable two-way energy flow with the grid. We propose a new
energy cooperation paradigm, where a group of BSs dynamically share their
renewable energy for more efficient operation via locally injecting/drawing
power to/from an aggregator with a zero effective sum-energy exchanged. Under
this new energy cooperation model, we consider the downlink transmission in one
CoMP cluster with cooperative zero-forcing (ZF) based precoding at the BSs. We
maximize the weighted sum-rate for all MTs by jointly optimizing the transmit
power allocations at cooperative BSs and their exchanged energy amounts subject
to a new type of power constraints featuring energy cooperation among BSs with
practical loss ratios. Our new setup with BSs' energy cooperation generalizes
the conventional CoMP transmit optimization under BSs' sum-power or
individual-power constraints. Finally, we validate our results by simulations
under various practical setups, and show that the proposed joint communication
and energy cooperation scheme substantially improves the downlink throughput of
CoMP systems powered by smart grid and renewable energy, as compared to other
suboptimal designs without communication and/or energy cooperation.
|
1303.2873 | Inferring Social Rank in an Old Assyrian Trade Network | cs.CY cs.SI | We present work in jointly inferring the unique individuals as well as their
social rank within a collection of letters from an Old Assyrian trade colony in
K\"ultepe, Turkey, settled by merchants from the ancient city of Assur for
approximately 200 years between 1950-1750 BCE, the height of the Middle Bronze
Age. Using a probabilistic latent-variable model, we leverage pairwise social
differences between names in cuneiform tablets to infer a single underlying
social order that best explains the data we observe. Evaluating our output with
published judgments by domain experts suggests that our method may be used for
building informed hypotheses that are driven by data, and that may offer
promising avenues for directed research by Assyriologists.
|
1303.2912 | Integrated Pre-Processing for Bayesian Nonlinear System Identification
with Gaussian Processes | cs.AI cs.RO cs.SY stat.ML | We introduce GP-FNARX: a new model for nonlinear system identification based
on a nonlinear autoregressive exogenous model (NARX) with filtered regressors
(F) where the nonlinear regression problem is tackled using sparse Gaussian
processes (GP). We integrate data pre-processing with system identification
into a fully automated procedure that goes from raw data to an identified
model. Both pre-processing parameters and GP hyper-parameters are tuned by
maximizing the marginal likelihood of the probabilistic model. We obtain a
Bayesian model of the system's dynamics which is able to report its uncertainty
in regions where the data is scarce. The automated approach, the modeling of
uncertainty and its relatively low computational cost make of GP-FNARX a good
candidate for applications in robotics and adaptive control.
|
1303.2933 | Interference Networks: A Complex System View | cs.IT math.IT | This paper presents an unusual view of interference wireless networks based
on complex system thinking. To proceed with this analysis, a literature review
of the different applications of complex systems is firstly presented to
illustrate how such an approach can be used in a wide range of research topics,
from economics to linguistics. Then the problem of quantifying the fundamental
limits of wireless systems where the co-channel interference is the main
limiting factor is described and hence contextualized in the perspective of
complex systems. Specifically some possible internal and external pressures
that the network elements may suffer are identified as, for example, queue
stability, maximum packet loss rate and transmit power constraint. Besides,
other important external factors such as mobility and incoming traffic are also
pointed out. As a study case, a decentralized point-to-point interference
network is described and several claims about the optimal design setting for
different network states and under two mobility conditions, namely quasi-static
and highly mobile, are stated based on results found in the literature. Using
these claims as a background, the design of a robust adaptive algorithm that
each network element should run is investigated.
|
1303.2975 | Towards Automated Proof Strategy Generalisation | cs.LO cs.AI | The ability to automatically generalise (interactive) proofs and use such
generalisations to discharge related conjectures is a very hard problem which
remains unsolved. Here, we develop a notion of goal types to capture key
properties of goals, which enables abstractions over the specific order and
number of sub-goals arising when composing tactics. We show that the goal types
form a lattice, and utilise this property in the techniques we develop to
automatically generalise proof strategies in order to reuse it for proofs of
related conjectures. We illustrate our approach with an example.
|
1303.2987 | On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning
with Application to Gene Regulatory Network Control | cs.SY math.OC | In this paper, we consider the periodic reference tracking problem in the
framework of batch-mode reinforcement learning, which studies methods for
solving optimal control problems from the sole knowledge of a set of
trajectories. In particular, we extend an existing batch-mode reinforcement
learning algorithm, known as Fitted Q Iteration, to the periodic reference
tracking problem. The presented periodic reference tracking algorithm
explicitly exploits a priori knowledge of the future values of the reference
trajectory and its periodicity. We discuss the properties of our approach and
illustrate it on the problem of reference tracking for a synthetic biology gene
regulatory network known as the generalised repressilator. This system can
produce decaying but long-lived oscillations, which makes it an interesting
system for the tracking problem. In our companion paper we also take a look at
the regulation problem of the toggle switch system, where the main goal is to
drive the system's states to a specific bounded region in the state space.
|
1303.3036 | Type-theoretical natural language semantics: on the system F for meaning
assembly | cs.LO cs.CL math.LO | This paper presents and extends our type theoretical framework for a
compositional treatment of natural language semantics with some lexical
features like coercions (e.g. of a town into a football club) and copredication
(e.g. on a town as a set of people and as a location). The second order typed
lambda calculus was shown to be a good framework, and here we discuss how to
introduced predefined types and coercive subtyping which are much more natural
than internally coded similar constructs. Linguistic applications of these new
features are also exemplified.
|
1303.3047 | Data Retrieval over DNS in SQL Injection Attacks | cs.CR cs.DB cs.NI | This paper describes an advanced SQL injection technique where DNS resolution
process is exploited for retrieval of malicious SQL query results. Resulting
DNS requests are intercepted by attackers themselves at the controlled remote
name server extracting valuable data. Open source SQL injection tool sqlmap has
been adjusted to automate this task. With modifications done, attackers are
able to use this technique for fast and low profile data retrieval, especially
in cases where other standard ones fail.
|
1303.3049 | On Optimal Jamming Over an Additive Noise Channel | math.OC cs.IT cs.SY math.IT | This paper considers the problem of optimal zero-delay jamming over an
additive noise channel. Early work had already solved this problem for a
Gaussian source and channel. Building on a sequence of recent results on
conditions for linearity of optimal estimation, and of optimal mappings in
source-channel coding, we derive the saddle-point solution to the jamming
problem for general sources and channels, without recourse to Gaussian
assumptions. We show that linearity conditions play a pivotal role in jamming,
in the sense that the optimal jamming strategy is to effectively force both
transmitter and receiver to default to linear mappings, i.e., the jammer
ensures, whenever possible, that the transmitter and receiver cannot benefit
from non-linear strategies. This result is shown to subsume the known result
for Gaussian source and channel. We analyze conditions and general settings
where such unbeatable strategy can indeed be achieved by the jammer. Moreover,
we provide the procedure to approximate optimal jamming in the remaining
(source-channel) cases where the jammer cannot impose linearity on the
transmitter and the receiver.
|
1303.3055 | Online Learning in Markov Decision Processes with Adversarially Chosen
Transition Probability Distributions | cs.LG stat.ML | We study the problem of learning Markov decision processes with finite state
and action spaces when the transition probability distributions and loss
functions are chosen adversarially and are allowed to change with time. We
introduce an algorithm whose regret with respect to any policy in a comparison
class grows as the square root of the number of rounds of the game, provided
the transition probabilities satisfy a uniform mixing condition. Our approach
is efficient as long as the comparison class is polynomial and we can compute
expectations over sample paths for each policy. Designing an efficient
algorithm with small regret for the general case remains an open problem.
|
1303.3058 | Robust Auxiliary Vector Filtering with Constrained Constant Modulus
Design for Beamforming | cs.IT math.IT | This paper proposes an auxiliary vector filtering (AVF) algorithm based on a
constrained constant modulus (CCM) design for robust adaptive beamforming. This
scheme provides an efficient way to deal with filters with a large number of
elements. The proposed beamformer decomposes the adaptive filter into a
constrained (reference vector filters) and an unconstrained (auxiliary vector
filters) components. The weight vector is iterated by subtracting the scaling
auxiliary vector from the reference vector. The scalar factor and the auxiliary
vector depend on each other and are jointly calculated according to the CCM
criterion. The proposed robust AVF algorithm provides an iterative exchange of
information between the scalar factor and the auxiliary vector and thus leads
to a fast convergence and an improved steady-state performance over the
existing techniques. Simulations are performed to show the performance and the
robustness of the proposed scheme and algorithm in several scenarios.
|
1303.3067 | Computing Motion with 3D Memristive Grid | cs.CV q-bio.NC | Computing the relative motion of objects is an important navigation task that
we routinely perform by relying on inherently unreliable biological cells in
the retina. The non-linear and adaptive response of memristive devices make
them excellent building blocks for realizing complex synaptic-like
architectures that are common in the human retina. Here, we introduce a novel
memristive thresholding scheme that facilitates the detection of moving edges.
In addition, a double-layered 3-D memristive network is employed for modeling
the motion computations that take place in both the Outer Plexiform Layer (OPL)
and Inner Plexiform Layer (IPL) that enables the detection of on-center and
off-center transient responses. Applying the transient detection results, it is
shown that it is possible to generate an estimation of the speed and direction
a moving object.
|
1303.3072 | Optical Flow Sensing and the Inverse Perception Problem for Flying Bats | cs.SY | The movements of birds, bats, and other flying species are governed by
complex sensorimotor systems that allow the animals to react to stationary
environmental features as well as to wind disturbances, other animals in nearby
airspace, and a wide variety of unexpected challenges. The paper and talk will
describe research that analyzes the three-dimensional trajectories of bats
flying in a habitat in Texas. The trajectories are computed with stereoscopic
methods using data from synchronous thermal videos that were recorded with high
temporal and spatial resolution from three viewpoints. Following our previously
reported work, we examine the possibility that bat trajectories in this habitat
are governed by optical flow sensing that interpolates periodic distance
measurements from echolocation. Using an idealized geometry of bat eyes, we
introduce the concept of time-to-transit, and recall some research that
suggests that this quantity is computed by the animals' visual cortex. Several
steering control laws based on time-to-transit are proposed for an idealized
flight model, and it is shown that these can be used to replicate the observed
flight of what we identify as typical bats. Although the vision-based motion
control laws we propose and the protocols for switching between them are quite
simple, some of the trajectories that have been synthesized are qualitatively
bat-like. Examination of the control protocols that generate these trajectories
suggests that bat motions are governed both by their reactions to a subset of
key feature points as well by their memories of where these feature points are
located.
|
1303.3087 | Statistical Texture Features based Handwritten and Printed Text
Classification in South Indian Documents | cs.CV | In this paper, we use statistical texture features for handwritten and
printed text classification. We primarily aim for word level classification in
south Indian scripts. Words are first extracted from the scanned document. For
each extracted word, statistical texture features are computed such as mean,
standard deviation, smoothness, moment, uniformity, entropy and local range
including local entropy. These feature vectors are then used to classify words
via k-NN classifier. We have validated the approach over several different
datasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e., Devanagari
are primarily employed where an average classification rate of 99.26% is
achieved. In addition, to provide an extensibility of the approach, we address
Roman script by using publicly available dataset and interesting results are
reported.
|
1303.3100 | Ergodic Interference Alignment with Delayed Feedback | cs.IT math.IT | We propose new ergodic interference alignment techniques for $K$-user
interference channels with delayed feedback. Two delayed feedback scenarios are
considered -- delayed channel information at transmitter (CIT) and delayed
output feedback. It is proved that the proposed techniques achieve total
$2K/(K+2)$ DoF which is higher than that by the retrospective interference
alignment for the delayed feedback scenarios.
|
1303.3134 | Egocentric vision IT technologies for Alzheimer disease assessment and
studies | cs.HC cs.CV | Egocentric vision technology consists in capturing the actions of persons
from their own visual point of view using wearable camera sensors. We apply
this new paradigm to instrumental activities monitoring with the objective of
providing new tools for the clinical evaluation of the impact of the disease on
persons with dementia. In this paper, we introduce the current state of the
development of this technology and focus on two technology modules: automatic
location estimation and visual saliency estimation for content interpretation.
|
1303.3145 | Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC
Performance | cs.NE | ROC is usually used to analyze the performance of classifiers in data mining.
ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical
ROC curve, and covers potential optima for the given set of classifiers.
Generally, ROC performance maximization could be considered to maximize the
ROCCH, which also means to maximize the true positive rate (tpr) and minimize
the false positive rate (fpr) for each classifier in the ROC space. However,
tpr and fpr are conflicting with each other in the ROCCH optimization process.
Though ROCCH maximization problem seems like a multi-objective optimization
problem (MOP), the special characters make it different from traditional MOP.
In this work, we will discuss the difference between them and propose convex
hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH
maximization problems. Convex hull-based sort is an indicator based selection
scheme that aims to maximize the area under convex hull, which serves as a
unary indicator for the performance of a set of points. A selection procedure
is described that can be efficiently implemented and follows similar design
principles than classical hyper-volume based optimization algorithms. It is
hypothesized that by using a tailored indicator-based selection scheme CH-MOGP
gets more efficient for ROC convex hull approximation than algorithms which
compute all Pareto optimal points. To test our hypothesis we compare the new
CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D)
and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine
learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results
based on 22 well-known UCI data sets show that CH-MOGP outperforms
significantly traditional EMOAs.
|
1303.3152 | Material quality assessment of silk nanofibers based on swarm
intelligence | cs.CV | In this paper, we propose a novel approach for texture analysis based on
artificial crawler model. Our method assumes that each agent can interact with
the environment and each other. The evolution process converges to an
equilibrium state according to the set of rules. For each textured image, the
feature vector is composed by signatures of the live agents curve at each time.
Experimental results revealed that combining the minimum and maximum signatures
into one increase the classification rate. In addition, we pioneer the use of
autonomous agents for characterizing silk fibroin scaffolds. The results
strongly suggest that our approach can be successfully employed for texture
analysis.
|
1303.3154 | Mixed Strategy May Outperform Pure Strategy: An Initial Study | cs.NE cs.GT | In pure strategy meta-heuristics, only one search strategy is applied for all
time. In mixed strategy meta-heuristics, each time one search strategy is
chosen from a strategy pool with a probability and then is applied. An example
is classical genetic algorithms, where either a mutation or crossover operator
is chosen with a probability each time. The aim of this paper is to compare the
performance between mixed strategy and pure strategy meta-heuristic algorithms.
First an experimental study is implemented and results demonstrate that mixed
strategy evolutionary algorithms may outperform pure strategy evolutionary
algorithms on the 0-1 knapsack problem in up to 77.8% instances. Then
Complementary Strategy Theorem is rigorously proven for applying mixed strategy
at the population level. The theorem asserts that given two meta-heuristic
algorithms where one uses pure strategy 1 and another uses pure strategy 2, the
condition of pure strategy 2 being complementary to pure strategy 1 is
sufficient and necessary if there exists a mixed strategy meta-heuristics
derived from these two pure strategies and its expected number of generations
to find an optimal solution is no more than that of using pure strategy 1 for
any initial population, and less than that of using pure strategy 1 for some
initial population.
|
1303.3163 | A Greedy Approximation of Bayesian Reinforcement Learning with Probably
Optimistic Transition Model | cs.AI cs.LG stat.ML | Bayesian Reinforcement Learning (RL) is capable of not only incorporating
domain knowledge, but also solving the exploration-exploitation dilemma in a
natural way. As Bayesian RL is intractable except for special cases, previous
work has proposed several approximation methods. However, these methods are
usually too sensitive to parameter values, and finding an acceptable parameter
setting is practically impossible in many applications. In this paper, we
propose a new algorithm that greedily approximates Bayesian RL to achieve
robustness in parameter space. We show that for a desired learning behavior,
our proposed algorithm has a polynomial sample complexity that is lower than
those of existing algorithms. We also demonstrate that the proposed algorithm
naturally outperforms other existing algorithms when the prior distributions
are not significantly misleading. On the other hand, the proposed algorithm
cannot handle greatly misspecified priors as well as the other algorithms can.
This is a natural consequence of the fact that the proposed algorithm is
greedier than the other algorithms. Accordingly, we discuss a way to select an
appropriate algorithm for different tasks based on the algorithms' greediness.
We also introduce a new way of simplifying Bayesian planning, based on which
future work would be able to derive new algorithms.
|
1303.3164 | Features and Aggregators for Web-scale Entity Search | cs.IR | We focus on two research issues in entity search: scoring a document or
snippet that potentially supports a candidate entity, and aggregating scores
from different snippets into an entity score. Proximity scoring has been
studied in IR outside the scope of entity search. However, aggregation has been
hardwired except in a few cases where probabilistic language models are used.
We instead explore simple, robust, discriminative ranking algorithms, with
informative snippet features and broad families of aggregation functions. Our
first contribution is a study of proximity-cognizant snippet features. In
contrast with prior work which uses hardwired "proximity kernels" that
implement a fixed decay with distance, we present a "universal" feature
encoding which jointly expresses the perplexity (informativeness) of a query
term match and the proximity of the match to the entity mention. Our second
contribution is a study of aggregation functions. Rather than train the ranking
algorithm on snippets and then aggregate scores, we directly train on entities
such that the ranking algorithm takes into account the aggregation function
being used. Our third contribution is an extensive Web-scale evaluation of the
above algorithms on two data sets having quite different properties and
behavior. The first one is the W3C dataset used in TREC-scale enterprise
search, with pre-annotated entity mentions. The second is a Web-scale
open-domain entity search dataset consisting of 500 million Web pages, which
contain about 8 billion token spans annotated automatically with two million
entities from 200,000 entity types in Wikipedia. On the TREC dataset, the
performance of our system is comparable to the currently prevalent systems. On
the much larger and noisier Web dataset, our system delivers significantly
better performance than all other systems, with 8% MAP improvement over the
closest competitor.
|
1303.3165 | Joint Optimization of Throughput and Packet Drop Rate for Delay
Sensitive Applications in TDD Satellite Network Coded Systems | cs.IT cs.NI math.IT | In this paper, we consider the issue of throughput and packet drop rate (PDR)
optimization as two performance metrics for delay sensitive applications in
network coded time division duplex (TDD) satellite systems with large round
trip times (RTT). We adopt random linear network coding (RLNC) under two
different scenarios, feedback-less and with feedback, and our goal is to
jointly optimize the mean throughputs and PDRs of users in the system. For this
purpose, we propose a systematic framework and start with formulating and
optimizing these performance metrics for the single-user case. This framework
enables us to analytically compare the performance metrics under different
system parameters and settings. By comparing RLNC schemes under feedback-less
and feedback scenarios for different RTTs, we show that the feedback-less
schemes outperform the schemes with feedback in TDD systems with large RTTs.
Then, we extend the study of feedback-less RLNC schemes to the multi-user
broadcast case. Here, we consider a number of different broadcast scenarios and
optimize the system parameters such that the best overall performance is
achieved. Furthermore, the complicated interplay of the mean throughputs and
PDRs of different users with different packet erasure conditions in each of the
considered broadcast scenarios is discussed.
|
1303.3170 | Types and forgetfulness in categorical linguistics and quantum mechanics | cs.CL math.CT quant-ph | The role of types in categorical models of meaning is investigated. A general
scheme for how typed models of meaning may be used to compare sentences,
regardless of their grammatical structure is described, and a toy example is
used as an illustration. Taking as a starting point the question of whether the
evaluation of such a type system 'loses information', we consider the
parametrized typing associated with connectives from this viewpoint.
The answer to this question implies that, within full categorical models of
meaning, the objects associated with types must exhibit a simple but subtle
categorical property known as self-similarity. We investigate the category
theory behind this, with explicit reference to typed systems, and their
monoidal closed structure. We then demonstrate close connections between such
self-similar structures and dagger Frobenius algebras. In particular, we
demonstrate that the categorical structures implied by the polymorphically
typed connectives give rise to a (lax unitless) form of the special forms of
Frobenius algebras known as classical structures, used heavily in abstract
categorical approaches to quantum mechanics.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.