id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1309.5836 | Exact Joint Distribution Analysis of Zero-Forcing V-BLAST Gains with
Greedy Ordering | cs.IT math.IT | We derive the joint probability distribution of zero-forcing (ZF) V-BLAST
gains under a greedy selection of decoding order and no error propagation.
Unlike the previous approximated analyses, a mathematical framework is built by
applying order statistics rules and an exact closed-form joint probability
density function expression for squared layer gains is obtained. Our analysis
relies on the fact that all orderings are equiprobable under independent and
identical Rayleigh fading. Based on this idea, we determine the joint
distribution of the ordered gains from the joint distribution of the unordered
gains. Our results are applicable for any number of transmit and receive
antennas. Although we present our analysis in a ZF V-BLAST setting, our
analytical results can be directly applied for the dual cases of ZF V-BLAST.
Under the assumption of a low rate feedback of decoding order to the
transmitter, a benefit of having exact expressions is illustrated by the
calculation of the cutoff value under optimal power allocation that maximizes
the sum of the substream outage capacities under a given sum power constraint.
We provide numerical results and verify our analysis by means of simulations.
|
1309.5843 | Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet | cs.CL | Assigning a positive or negative score to a word out of context (i.e. a
word's prior polarity) is a challenging task for sentiment analysis. In the
literature, various approaches based on SentiWordNet have been proposed. In
this paper, we compare the most often used techniques together with newly
proposed ones and incorporate all of them in a learning framework to see
whether blending them can further improve the estimation of prior polarity
scores. Using two different versions of SentiWordNet and testing regression and
classification models across tasks and datasets, our learning approach
consistently outperforms the single metrics, providing a new state-of-the-art
approach in computing words' prior polarity for sentiment analysis. We conclude
our investigation showing interesting biases in calculated prior polarity
scores when word Part of Speech and annotator gender are considered.
|
1309.5854 | Demodulation of Sparse PPM Signals with Low Samples Using Trained RIP
Matrix | cs.OH cs.IT cs.LG math.IT | Compressed sensing (CS) theory considers the restricted isometry property
(RIP) as a sufficient condition for measurement matrix which guarantees the
recovery of any sparse signal from its compressed measurements. The RIP
condition also preserves enough information for classification of sparse
symbols, even with fewer measurements. In this work, we utilize RIP bound as
the cost function for training a simple neural network in order to exploit the
near optimal measurements or equivalently near optimal features for
classification of a known set of sparse symbols. As an example, we consider
demodulation of pulse position modulation (PPM) signals. The results indicate
that the proposed method has much better performance than the random
measurements and requires less samples than the optimum matched filter
demodulator, at the expense of some performance loss. Further, the proposed
approach does not need equalizer for multipath channels in contrast to the
conventional receiver.
|
1309.5868 | A Framework for Structural Input/Output and Control Configuration
Selection in Large-Scale Systems | math.OC cs.SY | This paper addresses problems on the structural design of control systems
taking explicitly into consideration the possible application to large-scale
systems. We provide an efficient and unified framework to solve the following
major minimization problems: (i) selection of the minimum number of
manipulated/measured variables to achieve structural
controllability/observability of the system, and (ii) selection of the minimum
number of feedback interconnections between measured and manipulated variables
such that the closed-loop system has no structurally fixed modes. Contrary to
what would be expected, we show that it is possible to obtain a global solution
for each of the aforementioned minimization problems using polynomial
complexity algorithms in the number of the state variables of the system. In
addition, we provide several new graph-theoretic characterizations of
structural systems concepts, which, in turn, enable us to characterize all
possible solutions to the above problems.
|
1309.5896 | On the Success Rate of Crossover Operators for Genetic Programming with
Offspring Selection | cs.NE | Genetic programming is a powerful heuristic search technique that is used for
a number of real world applications to solve among others regression,
classification, and time-series forecasting problems. A lot of progress towards
a theoretic description of genetic programming in form of schema theorems has
been made, but the internal dynamics and success factors of genetic programming
are still not fully understood. In particular, the effects of different
crossover operators in combination with offspring selection are largely
unknown.
This contribution sheds light on the ability of well-known GP crossover
operators to create better offspring when applied to benchmark problems. We
conclude that standard (sub-tree swapping) crossover is a good default choice
in combination with offspring selection, and that GP with offspring selection
and random selection of crossover operators can improve the performance of the
algorithm in terms of best solution quality when no solution size constraints
are applied.
|
1309.5904 | Fenchel Duals for Drifting Adversaries | cs.LG | We describe a primal-dual framework for the design and analysis of online
convex optimization algorithms for {\em drifting regret}. Existing literature
shows (nearly) optimal drifting regret bounds only for the $\ell_2$ and the
$\ell_1$-norms. Our work provides a connection between these algorithms and the
Online Mirror Descent ($\omd$) updates; one key insight that results from our
work is that in order for these algorithms to succeed, it suffices to have the
gradient of the regularizer to be bounded (in an appropriate norm). For
situations (like for the $\ell_1$ norm) where the vanilla regularizer does not
have this property, we have to {\em shift} the regularizer to ensure this.
Thus, this helps explain the various updates presented in \cite{bansal10,
buchbinder12}. We also consider the online variant of the problem with
1-lookahead, and with movement costs in the $\ell_2$-norm. Our primal dual
approach yields nearly optimal competitive ratios for this problem.
|
1309.5909 | From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels
and Fairy Tales | cs.CL | Today we have access to unprecedented amounts of literary texts. However,
search still relies heavily on key words. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in both individual books and across very large collections. We
introduce the concept of emotion word density, and using the Brothers Grimm
fairy tales as example, we show how collections of text can be organized for
better search. Using the Google Books Corpus we show how to determine an
entity's emotion associations from co-occurring words. Finally, we compare
emotion words in fairy tales and novels, to show that fairy tales have a much
wider range of emotion word densities than novels.
|
1309.5914 | Computational barriers in minimax submatrix detection | math.ST cs.IT math.IT stat.TH | This paper studies the minimax detection of a small submatrix of elevated
mean in a large matrix contaminated by additive Gaussian noise. To investigate
the tradeoff between statistical performance and computational cost from a
complexity-theoretic perspective, we consider a sequence of discretized models
which are asymptotically equivalent to the Gaussian model. Under the hypothesis
that the planted clique detection problem cannot be solved in randomized
polynomial time when the clique size is of smaller order than the square root
of the graph size, the following phase transition phenomenon is established:
when the size of the large matrix $p\to\infty$, if the submatrix size
$k=\Theta(p^{\alpha})$ for any $\alpha\in(0,{2}/{3})$, computational complexity
constraints can incur a severe penalty on the statistical performance in the
sense that any randomized polynomial-time test is minimax suboptimal by a
polynomial factor in $p$; if $k=\Theta(p^{\alpha})$ for any
$\alpha\in({2}/{3},1)$, minimax optimal detection can be attained within
constant factors in linear time. Using Schatten norm loss as a representative
example, we show that the hardness of attaining the minimax estimation rate can
crucially depend on the loss function. Implications on the hardness of support
recovery are also obtained.
|
1309.5927 | XML Compression via DAGs | cs.DS cs.DB | Unranked trees can be represented using their minimal dag (directed acyclic
graph). For XML this achieves high compression ratios due to their repetitive
mark up. Unranked trees are often represented through first child/next sibling
(fcns) encoded binary trees. We study the difference in size (= number of
edges) of minimal dag versus minimal dag of the fcns encoded binary tree. One
main finding is that the size of the dag of the binary tree can never be
smaller than the square root of the size of the minimal dag, and that there are
examples that match this bound. We introduce a new combined structure, the
hybrid dag, which is guaranteed to be smaller than (or equal in size to) both
dags. Interestingly, we find through experiments that last child/previous
sibling encodings are much better for XML compression via dags, than fcns
encodings. We determine the average sizes of unranked and binary dags over a
given set of labels (under uniform distribution) in terms of their exact
generating functions, and in terms of their asymptotical behavior.
|
1309.5931 | Data Mining using Unguided Symbolic Regression on a Blast Furnace
Dataset | cs.NE | In this paper a data mining approach for variable selection and knowledge
extraction from datasets is presented. The approach is based on unguided
symbolic regression (every variable present in the dataset is treated as the
target variable in multiple regression runs) and a novel variable relevance
metric for genetic programming. The relevance of each input variable is
calculated and a model approximating the target variable is created. The
genetic programming configurations with different target variables are executed
multiple times to reduce stochastic effects and the aggregated results are
displayed as a variable interaction network. This interaction network
highlights important system components and implicit relations between the
variables. The whole approach is tested on a blast furnace dataset, because of
the complexity of the blast furnace and the many interrelations between the
variables. Finally the achieved results are discussed with respect to existing
knowledge about the blast furnace process.
|
1309.5942 | Colourful Language: Measuring Word-Colour Associations | cs.CL | Since many real-world concepts are associated with colour, for example danger
with red, linguistic information is often complimented with the use of
appropriate colours in information visualization and product marketing. Yet,
there is no comprehensive resource that captures concept-colour associations.
We present a method to create a large word-colour association lexicon by
crowdsourcing. We focus especially on abstract concepts and emotions to show
that even though they cannot be physically visualized, they too tend to have
strong colour associations. Finally, we show how word-colour associations
manifest themselves in language, and quantify usefulness of co-occurrence and
polarity cues in automatically detecting colour associations.
|
1309.5979 | Asymptotic Analysis of LASSOs Solution Path with Implications for
Approximate Message Passing | math.ST cs.IT math.IT stat.ML stat.TH | This paper concerns the performance of the LASSO (also knows as basis pursuit
denoising) for recovering sparse signals from undersampled, randomized, noisy
measurements. We consider the recovery of the signal $x_o \in \mathbb{R}^N$
from $n$ random and noisy linear observations $y= Ax_o + w$, where $A$ is the
measurement matrix and $w$ is the noise. The LASSO estimate is given by the
solution to the optimization problem $x_o$ with $\hat{x}_{\lambda} = \arg
\min_x \frac{1}{2} \|y-Ax\|_2^2 + \lambda \|x\|_1$. Despite major progress in
the theoretical analysis of the LASSO solution, little is known about its
behavior as a function of the regularization parameter $\lambda$. In this paper
we study two questions in the asymptotic setting (i.e., where $N \rightarrow
\infty$, $n \rightarrow \infty$ while the ratio $n/N$ converges to a fixed
number in $(0,1)$): (i) How does the size of the active set
$\|\hat{x}_\lambda\|_0/N$ behave as a function of $\lambda$, and (ii) How does
the mean square error $\|\hat{x}_{\lambda} - x_o\|_2^2/N$ behave as a function
of $\lambda$? We then employ these results in a new, reliable algorithm for
solving LASSO based on approximate message passing (AMP).
|
1309.5984 | An evolutionary approach to Function | cs.AI | Background: Understanding the distinction between function and role is vexing
and difficult. While it appears to be useful, in practice this distinction is
hard to apply, particularly within biology.
Results: I take an evolutionary approach, considering a series of examples,
to develop and generate definitions for these concepts. I test them in practice
against the Ontology for Biomedical Investigations (OBI). Finally, I give an
axiomatisation and discuss methods for applying these definitions in practice.
Conclusions: The definitions in this paper are applicable, formalizing
current practice. As such, they make a significant contribution to the use of
these concepts within biomedical ontologies.
|
1309.5993 | Combining smart card data and household travel survey to analyze
jobs-housing relationships in Beijing | cs.SI physics.soc-ph | Location Based Services (LBS) provide a new perspective for spatiotemporally
analyzing dynamic urban systems. Research has investigated urban dynamics using
GSM (Global System for Mobile Communications), GPS (Global Positioning System),
SNS (Social Networking Services) and Wi-Fi techniques. However, less attention
has been paid to the analysis of urban structure (especially commuting pattern)
using smart card data (SCD), which are widely available in most cities.
Additionally, ubiquitous LBS data, although providing rich spatial and temporal
information, lacks rich information on the social dimension, which limits its
in-depth application. To bridge this gap, this paper combines bus SCD for a
one-week period with a one-day household travel survey, as well as a
parcel-level land use map to identify job-housing locations and commuting trip
routes in Beijing. Two data forms (TRIP and PTD) are proposed, with PTD used
for jobs-housing identification and TRIP used for commuting trip route
identification. The results of the identification are aggregated in the bus
stop and traffic analysis zone (TAZ) scales, respectively. Particularly,
commuting trips from three typical residential communities to six main business
zones are mapped and compared to analyze commuting patterns in Beijing. The
identified commuting trips are validated on three levels by comparison with
those from the survey in terms of commuting time and distance, and the positive
validation results prove the applicability of our approach. Our experiment, as
a first step toward enriching LBS data using conventional survey and urban GIS
data, can obtain solid identification results based on rules extracted from
existing surveys or censuses.
|
1309.6001 | Co-evolutionary dynamics in social networks: A case study of Twitter | cs.SI physics.soc-ph | Complex networks often exhibit co-evolutionary dynamics, meaning that the
network topology and the state of nodes or links are coupled, affecting each
other in overlapping time scales. We focus on the co-evolutionary dynamics of
online social networks, and on Twitter in particular. Monitoring the activity
of thousands of Twitter users in real-time, and tracking their followers and
tweets/retweets, we propose a method to infer new retweet-driven follower
relations. The formation of such relations is much more likely than the
exogenous creation of new followers in the absence of any retweets. We identify
the most significant factors (reciprocity and the number of retweets that a
potential new follower receives) and propose a simple probabilistic model of
this effect. We also discuss the implications of such co-evolutionary dynamics
on the topology and function of a social network.
|
1309.6027 | Energy-Efficient Optimization for Wireless Information and Power
Transfer in Large-Scale MIMO Systems Employing Energy Beamforming | cs.IT math.IT | In this letter, we consider a large-scale multiple-input multiple-output
(MIMO) system where the receiver should harvest energy from the transmitter by
wireless power transfer to support its wireless information transmission. The
energy beamforming in the large-scale MIMO system is utilized to address the
challenging problem of long-distance wireless power transfer. Furthermore,
considering the limitation of the power in such a system, this letter focuses
on the maximization of the energy efficiency of information transmission (bit
per Joule) while satisfying the quality-of-service (QoS) requirement, i.e.
delay constraint, by jointly optimizing transfer duration and transmit power.
By solving the optimization problem, we derive an energy-efficient resource
allocation scheme. Numerical results validate the effectiveness of the proposed
scheme.
|
1309.6036 | Almost Linear Complexity Methods for Delay-Doppler Channel Estimation | cs.IT math.IT | A fundamental task in wireless communication is channel estimation: Compute
the channel parameters a signal undergoes while traveling from a transmitter to
a receiver. In the case of delay-Doppler channel, i.e., a signal undergoes only
delay and Doppler shifts, a widely used method to compute delay-Doppler
parameters is the pseudo-random method. It uses a pseudo-random sequence of
length N; and, in case of non-trivial relative velocity between transmitter and
receiver, its computational complexity is O(N^2logN) arithmetic operations. In
[1] the flag method was introduced to provide a faster algorithm for
delay-Doppler channel estimation. It uses specially designed flag sequences and
its complexity is O(rNlogN) for channels of sparsity r. In these notes, we
introduce the incidence and cross methods for channel estimation. They use
triple-chirp and double-chirp sequences of length N, correspondingly. These
sequences are closely related to chirp sequences widely used in radar systems.
The arithmetic complexity of the incidence and cross methods is O(NlogN + r^3),
and O(NlogN + r^2), respectively.
|
1309.6041 | Persistent Monitoring of Events with Stochastic Arrivals at Multiple
Stations | cs.RO | This paper introduces a new mobile sensor scheduling problem, involving a
single robot tasked with monitoring several events of interest that occur at
different locations. Of particular interest is the monitoring of transient
events that can not be easily forecast. Application areas range from natural
phenomena ({\em e.g.}, monitoring abnormal seismic activity around a volcano
using a ground robot) to urban activities ({\em e.g.}, monitoring early
formations of traffic congestion using an aerial robot). Motivated by those and
many other examples, this paper focuses on problems in which the precise
occurrence times of the events are unknown {\em a priori}, but statistics for
their inter-arrival times are available. The robot's task is to monitor the
events to optimize the following two objectives: {\em (i)} maximize the number
of events observed and {\em (ii)} minimize the delay between two consecutive
observations of events occurring at the same location. The paper considers the
case when a robot is tasked with optimizing the event observations in a
balanced manner, following a cyclic patrolling route. First, assuming the
cyclic ordering of stations is known, we prove the existence and uniqueness of
the optimal solution, and show that the optimal solution has desirable
convergence and robustness properties. Our constructive proof also produces an
efficient algorithm for computing the unique optimal solution with $O(n)$ time
complexity, in which $n$ is the number of stations, with $O(\log n)$ time
complexity for incrementally adding or removing stations. Except for the
algorithm, most of the analysis remains valid when the cyclic order is unknown.
We then provide a polynomial-time approximation scheme that gives a
$(1+\epsilon)$-optimal solution for this more general, NP-hard problem.
|
1309.6047 | Non-negative Matrix Factorization with Linear Constraints for
Single-Channel Speech Enhancement | cs.SD cs.CL | This paper investigates a non-negative matrix factorization (NMF)-based
approach to the semi-supervised single-channel speech enhancement problem where
only non-stationary additive noise signals are given. The proposed method
relies on sinusoidal model of speech production which is integrated inside NMF
framework using linear constraints on dictionary atoms. This method is further
developed to regularize harmonic amplitudes. Simple multiplicative algorithms
are presented. The experimental evaluation was made on TIMIT corpus mixed with
various types of noise. It has been shown that the proposed method outperforms
some of the state-of-the-art noise suppression techniques in terms of
signal-to-noise ratio.
|
1309.6073 | Improved Analyses for SP and CoSaMP Algorithms in Terms of Restricted
Isometry Constants | cs.IT math.IT | In the context of compressed sensing (CS), both Subspace Pursuit (SP) and
Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative
greedy recovery algorithms which could reduce the recovery complexity greatly
comparing with the well-known $\ell_1$-minimization. Restricted isometry
property (RIP) and restricted isometry constant (RIC) of measurement matrices
which ensure the convergency of iterative algorithms play key roles for the
guarantee of successful reconstructions. In this paper, we show that for the
$s$-sparse recovery, the RICs are enlarged to $\delta_{3s}<0.4859$ for SP and
$\delta_{4s}<0.5$ for CoSaMP, which improve the known results significantly.
The proposed results also apply to almost sparse signal and corrupted
measurements.
|
1309.6109 | Analysis of Scientific Cloud Computing requirements | cs.DC cs.CE | While the requirements of enterprise and web applications have driven the
development of Cloud computing, some of its key features, such as customized
environments and rapid elasticity, could also benefit scientific applications.
However, neither virtualization techniques nor Cloud-like access to resources
is common in scientific computing centers due to the negative perception of the
impact that virtualization techniques introduce.
In this paper we discuss the feasibility of the IaaS cloud model to satisfy
some of the computational science requirements and the main drawbacks that need
to be addressed by cloud resource providers so that the maximum benefit can be
obtained from a given cloud infrastructure.
|
1309.6129 | Partition-Merge: Distributed Inference and Modularity Optimization | cs.DS cs.AI cs.SI | This paper presents a novel meta algorithm, Partition-Merge (PM), which takes
existing centralized algorithms for graph computation and makes them
distributed and faster. In a nutshell, PM divides the graph into small
subgraphs using our novel randomized partitioning scheme, runs the centralized
algorithm on each partition separately, and then stitches the resulting
solutions to produce a global solution. We demonstrate the efficiency of the PM
algorithm on two popular problems: computation of Maximum A Posteriori (MAP)
assignment in an arbitrary pairwise Markov Random Field (MRF), and modularity
optimization for community detection. We show that the resulting distributed
algorithms for these problems essentially run in time linear in the number of
nodes in the graph, and perform as well -- or even better -- than the original
centralized algorithm as long as the graph has geometric structures. Here we
say a graph has geometric structures, or polynomial growth property, when the
number of nodes within distance r of any given node grows no faster than a
polynomial function of r. More precisely, if the centralized algorithm is a
C-factor approximation with constant C \ge 1, the resulting distributed
algorithm is a (C+\delta)-factor approximation for any small \delta>0; but if
the centralized algorithm is a non-constant (e.g. logarithmic) factor
approximation, then the resulting distributed algorithm becomes a constant
factor approximation. For general graphs, we compute explicit bounds on the
loss of performance of the resulting distributed algorithm with respect to the
centralized algorithm.
|
1309.6134 | Network analysis of the \'{I}slendinga s\"{o}gur - the Sagas of
Icelanders | physics.soc-ph cs.SI | The \'{I}slendinga s\"{o}gur - or Sagas of Icelanders - constitute a
collection of medieval literature set in Iceland around the late 9th to early
11th centuries, the so-called Saga Age. They purport to describe events during
the period around the settlement of Iceland and the generations immediately
following and constitute an important element of world literature thanks to
their unique narrative style. Although their historicity is a matter of
scholarly debate, the narratives contain interwoven and overlapping plots
involving thousands of characters and interactions between them. Here we
perform a network analysis of the \'{I}slendinga s\"{o}gur in an attempt to
gather quantitative information on interrelationships between characters and to
compare saga society to other social networks.
|
1309.6151 | Noise Weighting in the Design of {\Delta}{\Sigma} Modulators (with a
Psychoacoustic Coder as an Example) | cs.IT math.IT | A design flow for {\Delta}{\Sigma} modulators is illustrated, allowing
quantization noise to be shaped according to an arbitrary weighting profile.
Being based on FIR NTFs, possibly with high order, the flow is best suited for
digital architectures. The work builds on a recent proposal where the modulator
is matched to the reconstruction filter, showing that this type of optimization
can benefit a wide range of applications where noise (including in-band noise)
is known to have a different impact at different frequencies. The design of a
multiband modulator, a modulator avoiding DC noise, and an audio modulator
capable of distributing quantization artifacts according to a psychoacoustic
model are discussed as examples. A software toolbox is provided as a general
design aid and to replicate the proposed results.
|
1309.6162 | JRC-Names: A freely available, highly multilingual named entity resource | cs.CL | This paper describes a new, freely available, highly multilingual named
entity resource for person and organisation names that has been compiled over
seven years of large-scale multilingual news analysis combined with Wikipedia
mining, resulting in 205,000 per-son and organisation names plus about the same
number of spelling variants written in over 20 different scripts and in many
more languages. This resource, produced as part of the Europe Media Monitor
activity (EMM, http://emm.newsbrief.eu/overview.html), can be used for a number
of purposes. These include improving name search in databases or on the
internet, seeding machine learning systems to learn named entity recognition
rules, improve machine translation results, and more. We describe here how this
resource was created; we give statistics on its current size; we address the
issue of morphological inflection; and we give details regarding its
functionality. Updates to this resource will be made available daily.
|
1309.6176 | Feature Learning with Gaussian Restricted Boltzmann Machine for Robust
Speech Recognition | cs.CL cs.LG cs.SD | In this paper, we first present a new variant of Gaussian restricted
Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann
machine (MGRBM), with its definition and learning algorithm. Then we propose
using a learned GRBM or MGRBM to extract better features for robust speech
recognition. Our experiments on Aurora2 show that both GRBM-extracted and
MGRBM-extracted feature performs much better than Mel-frequency cepstral
coefficient (MFCC) with either HMM-GMM or hybrid HMM-deep neural network (DNN)
acoustic model, and MGRBM-extracted feature is slightly better.
|
1309.6185 | Acronym recognition and processing in 22 languages | cs.CL | We are presenting work on recognising acronyms of the form Long-Form
(Short-Form) such as "International Monetary Fund (IMF)" in millions of news
articles in twenty-two languages, as part of our more general effort to
recognise entities and their variants in news text and to use them for the
automatic analysis of the news, including the linking of related news across
languages. We show how the acronym recognition patterns, initially developed
for medical terms, needed to be adapted to the more general news domain and we
present evaluation results. We describe our effort to automatically merge the
numerous long-form variants referring to the same short-form, while keeping
non-related long-forms separate. Finally, we provide extensive statistics on
the frequency and the distribution of short-form/long-form pairs across
languages.
|
1309.6195 | Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via
the Complex-valued Fast Block Sparse Bayesian Learning Algorithm | cs.CV | Compressed Sensing based Terahertz imaging (CS-THz) is a computational
imaging technique. It uses only one THz receiver to accumulate the random
modulated image measurements where the original THz image is reconstruct from
these measurements using compressed sensing solvers. The advantage of the
CS-THz is its reduced acquisition time compared with the raster scan mode.
However, when it applied to large-scale two-dimensional (2D) imaging, the
increased dimension resulted in both high computational complexity and
excessive memory usage. In this paper, we introduced a novel CS-based THz
imaging system that progressively compressed the THz image column by column.
Therefore, the CS-THz system could be simplified with a much smaller sized
modulator and reduced dimension. In order to utilize the block structure and
the correlation of adjacent columns of the THz image, a complex-valued block
sparse Bayesian learning algorithm was proposed. We conducted systematic
evaluation of state-of-the-art CS algorithms under the scan based CS-THz
architecture. The compression ratios and the choices of the sensing matrices
were analyzed in detail using both synthetic and real-life THz images.
Simulation results showed that both the scan based architecture and the
proposed recovery algorithm were superior and efficient for large scale CS-THz
applications.
|
1309.6200 | On the Dispersions of the Gel'fand-Pinsker Channel and Dirty Paper
Coding | cs.IT math.IT | This paper studies second-order coding rates for memoryless channels with a
state sequence known non-causally at the encoder. In the case of finite
alphabets, an achievability result is obtained using constant-composition
random coding, and by using a small fraction of the block to transmit the type
of the state sequence. For error probabilities less than 1/2, it is shown that
the second-order rate improves on an existing one based on i.i.d. random
coding. In the Gaussian case (dirty paper coding) with an almost-sure power
constraint, an achievability result is obtained used using random coding over
the surface of a sphere, and using a small fraction of the block to transmit a
quantized description of the state power. It is shown that the second-order
asymptotics are identical to the single-user Gaussian channel of the same input
power without a state.
|
1309.6202 | Sentiment Analysis in the News | cs.CL | Recent years have brought a significant growth in the volume of research in
sentiment analysis, mostly on highly subjective text types (movie or product
reviews). The main difference these texts have with news articles is that their
target is clearly defined and unique across the text. Following different
annotation efforts and the analysis of the issues encountered, we realised that
news opinion mining is different from that of other text types. We identified
three subtasks that need to be addressed: definition of the target; separation
of the good and bad news content from the good and bad sentiment expressed on
the target; and analysis of clearly marked opinion that is expressed
explicitly, not needing interpretation or the use of world knowledge.
Furthermore, we distinguish three different possible views on newspaper
articles - author, reader and text, which have to be addressed differently at
the time of analysing sentiment. Given these definitions, we present work on
mining opinions about entities in English language news, in which (a) we test
the relative suitability of various sentiment dictionaries and (b) we attempt
to separate positive or negative opinion from good or bad news. In the
experiments described here, we tested whether or not subject domain-defining
vocabulary should be ignored. Results showed that this idea is more appropriate
in the context of news opinion mining and that the approaches taking this into
consideration produce a better performance.
|
1309.6204 | A Friendship Privacy Attack on Friends and 2-Distant Neighbors in Social
Networks | cs.SI cs.CR physics.soc-ph | In an undirected social graph, a friendship link involves two users and the
friendship is visible in both the users' friend lists. Such a dual visibility
of the friendship may raise privacy threats. This is because both users can
separately control the visibility of a friendship link to other users and their
privacy policies for the link may not be consistent. Even if one of them
conceals the link from a third user, the third user may find such a friendship
link from another user's friend list. In addition, as most users allow their
friends to see their friend lists in most social network systems, an adversary
can exploit the inconsistent policies to launch privacy attacks to identify and
infer many of a targeted user's friends. In this paper, we propose, analyze and
evaluate such an attack which is called Friendship Identification and Inference
(FII) attack. In a FII attack scenario, we assume that an adversary can only
see his friend list and the friend lists of his friends who do not hide the
friend lists from him. Then, a FII attack contains two attack steps: 1) friend
identification and 2) friend inference. In the friend identification step, the
adversary tries to identify a target's friends based on his friend list and
those of his friends. In the friend inference step, the adversary attempts to
infer the target's friends by using the proposed random walk with restart
approach. We present experimental results using three real social network
datasets and show that FII attacks are generally efficient and effective when
adversaries and targets are friends or 2-distant neighbors. We also
comprehensively analyze the attack results in order to find what values of
parameters and network features could promote FII attacks. Currently, most
popular social network systems with an undirected friendship graph, such as
Facebook, LinkedIn and Foursquare, are susceptible to FII attacks.
|
1309.6225 | Kinetics of node splitting in evolving complex networks | physics.soc-ph cond-mat.stat-mech cs.SI | We introduce a collection of complex networks generated by a combination of
preferential attachment and a previously unexamined process of "splitting"
nodes of degree $k$ into $k$ nodes of degree 1. Four networks are considered,
each evolves at each time step by either preferential attachment, with
probability $p$, or splitting with probability $1-p$. Two methods of attachment
are considered; first, attachment of an edge between a newly created node and
existing node in the network, and secondly by attachment of an edge between two
existing nodes. Splitting is also considered in two separate ways; first by
selecting each node with equal probability and secondly, selecting the node
with probability proportional to its degree. Exact solutions for the degree
distributions are found and scale-free structure is exhibited in those networks
where the candidates for splitting are chosen with uniform probability, those
that are chosen preferentially are distributed with a power law with
exponential cut-off.
|
1309.6226 | Automation of Mathematical Induction as part of the History of Logic | cs.AI | We review the history of the automation of mathematical induction
|
1309.6270 | Optimal Resource Allocation for Network Protection Against Spreading
Processes | math.OC cs.SY physics.soc-ph | We study the problem of containing spreading processes in arbitrary directed
networks by distributing protection resources throughout the nodes of the
network. We consider two types of protection resources are available: (i)
Preventive resources able to defend nodes against the spreading (such as
vaccines in a viral infection process), and (ii) corrective resources able to
neutralize the spreading after it has reached a node (such as antidotes). We
assume that both preventive and corrective resources have an associated cost
and study the problem of finding the cost-optimal distribution of resources
throughout the nodes of the network. We analyze these questions in the context
of viral spreading processes in directed networks. We study the following two
problems: (i) Given a fixed budget, find the optimal allocation of preventive
and corrective resources in the network to achieve the highest level of
containment, and (ii) when a budget is not specified, find the minimum budget
required to control the spreading process. We show that both resource
allocation problems can be solved in polynomial time using Geometric
Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide
class of cost functions. Furthermore, our approach allows to optimize
simultaneously over both preventive and corrective resources, even in the case
of cost functions being node-dependent. We illustrate our approach by designing
optimal protection strategies to contain an epidemic outbreak that propagates
through an air transportation network.
|
1309.6297 | Generating Explanations for Biomedical Queries | cs.AI cs.LO | We introduce novel mathematical models and algorithms to generate (shortest
or k different) explanations for biomedical queries, using answer set
programming. We implement these algorithms and integrate them in BIOQUERY-ASP.
We illustrate the usefulness of these methods with some complex biomedical
queries related to drug discovery, over the biomedical knowledge resources
PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY and ORPHADATA. To
appear in Theory and Practice of Logic Programming (TPLP).
|
1309.6301 | Solving OSCAR regularization problems by proximal splitting algorithms | cs.CV cs.LG stat.ML | The OSCAR (octagonal selection and clustering algorithm for regression)
regularizer consists of a L_1 norm plus a pair-wise L_inf norm (responsible for
its grouping behavior) and was proposed to encourage group sparsity in
scenarios where the groups are a priori unknown. The OSCAR regularizer has a
non-trivial proximity operator, which limits its applicability. We reformulate
this regularizer as a weighted sorted L_1 norm, and propose its grouping
proximity operator (GPO) and approximate proximity operator (APO), thus making
state-of-the-art proximal splitting algorithms (PSAs) available to solve
inverse problems with OSCAR regularization. The GPO is in fact the APO followed
by additional grouping and averaging operations, which are costly in time and
storage, explaining the reason why algorithms with APO are much faster than
that with GPO. The convergences of PSAs with GPO are guaranteed since GPO is an
exact proximity operator. Although convergence of PSAs with APO is may not be
guaranteed, we have experimentally found that APO behaves similarly to GPO when
the regularization parameter of the pair-wise L_inf norm is set to an
appropriately small value. Experiments on recovery of group-sparse signals
(with unknown groups) show that PSAs with APO are very fast and accurate.
|
1309.6307 | On the Non-Uniqueness of Solutions to the Average Cost HJB for
Controlled Diffusions with Near-Monotone Costs | cs.SY math.OC | We present a theorem for verification of optimality of controlled diffusions
under the average cost criterion with near-monotone running cost, without
invoking any blanket stability assumptions. The implications of this result to
the policy iteration algorithm are also discussed.
|
1309.6347 | Tracking Sentiment in Mail: How Genders Differ on Emotional Axes | cs.CL | With the widespread use of email, we now have access to unprecedented amounts
of text that we ourselves have written. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in many types of mail. We create a large word--emotion
association lexicon by crowdsourcing, and use it to compare emotions in love
letters, hate mail, and suicide notes. We show that there are marked
differences across genders in how they use emotion words in work-place email.
For example, women use many words from the joy--sadness axis, whereas men
prefer terms from the fear--trust axis. Finally, we show visualizations that
can help people track emotions in their emails.
|
1309.6348 | Decentralized identification and control of networks of coupled mobile
platforms through adaptive synchronization of chaos | nlin.AO cs.RO | In this paper we propose an application of adaptive synchronization of chaos
to detect changes in the topology of a mobile robotic network. We assume that
the network may evolve in time due to the relative motion of the mobile robots
and due to unknown environmental conditions, such as the presence of obstacles
in the environment. We consider that each robotic agent is equipped with a
chaotic oscillator whose state is propagated to the other robots through
wireless communication, with the goal of synchronizing the oscillators. We
introduce an adaptive strategy that each agent independently implements to: (i)
estimate the net coupling of all the oscillators in its neighborhood and (ii)
synchronize the state of the oscillators onto the same time evolution. We show
that by using this strategy, synchronization can be attained and changes in the
network topology can be detected. We go one step forward and consider the
possibility of using this information to control the mobile network. We show
the potential applicability of our technique to the problem of maintaining a
formation between a set of mobile platforms, which operate in an inhomogeneous
and uncertain environment. We discuss the importance of using chaotic
oscillators and validate our methodology by numerical simulations.
|
1309.6352 | Using Nuances of Emotion to Identify Personality | cs.CL | Past work on personality detection has shown that frequency of lexical
categories such as first person pronouns, past tense verbs, and sentiment words
have significant correlations with personality traits. In this paper, for the
first time, we show that fine affect (emotion) categories such as that of
excitement, guilt, yearning, and admiration are significant indicators of
personality. Additionally, we perform experiments to show that the gains
provided by the fine affect categories are not obtained by using coarse affect
categories alone or with specificity features alone. We employ these features
in five SVM classifiers for detecting five personality traits through essays.
We find that the use of fine emotion features leads to statistically
significant improvement over a competitive baseline, whereas the use of coarse
affect and specificity features does not.
|
1309.6369 | Predicting Adoption Probabilities in Social Networks | cs.SI physics.soc-ph | In a social network, adoption probability refers to the probability that a
social entity will adopt a product, service, or opinion in the foreseeable
future. Such probabilities are central to fundamental issues in social network
analysis, including the influence maximization problem. In practice, adoption
probabilities have significant implications for applications ranging from
social network-based target marketing to political campaigns; yet, predicting
adoption probabilities has not received sufficient research attention. Building
on relevant social network theories, we identify and operationalize key factors
that affect adoption decisions: social influence, structural equivalence,
entity similarity, and confounding factors. We then develop the
locally-weighted expectation-maximization method for Na\"ive Bayesian learning
to predict adoption probabilities on the basis of these factors. The principal
challenge addressed in this study is how to predict adoption probabilities in
the presence of confounding factors that are generally unobserved. Using data
from two large-scale social networks, we demonstrate the effectiveness of the
proposed method. The empirical results also suggest that cascade methods
primarily using social influence to predict adoption probabilities offer
limited predictive power, and that confounding factors are critical to adoption
probability predictions.
|
1309.6379 | Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of
Hybrid Diffusion Imaging based on BFOR Signal Basis | cs.CV | We propose a large deformation diffeomorphic metric mapping algorithm to
align multiple b-value diffusion weighted imaging (mDWI) data, specifically
acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then
propose a Bayesian model for estimating the white matter atlas from HYDIs. We
adopt the work given in Hosseinbor et al. (2012) and represent the q-space
diffusion signal with the Bessel Fourier orientation reconstruction (BFOR)
signal basis. The BFOR framework provides the representation of mDWI in the
q-space and thus reduces memory requirement. In addition, since the BFOR signal
basis is orthonormal, the L2 norm that quantifies the differences in the
q-space signals of any two mDWI datasets can be easily computed as the sum of
the squared differences in the BFOR expansion coefficients. In this work, we
show that the reorientation of the $q$-space signal due to spatial
transformation can be easily defined on the BFOR signal basis. We incorporate
the BFOR signal basis into the LDDMM framework and derive the gradient descent
algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally,
we extend the previous Bayesian atlas estimation framework for scalar-valued
images to HYDIs and derive the expectation-maximization algorithm for solving
the HYDI atlas estimation problem. Using real HYDI datasets, we show the
Bayesian model generates the white matter atlas with anatomical details.
Moreover, we show that it is important to consider the variation of mDWI
reorientation due to a small change in diffeomorphic transformation in the
LDDMM-HYDI optimization and to incorporate the full information of HYDI for
aligning mDWI.
|
1309.6390 | Contextually learnt detection of unusual motion-based behaviour in
crowded public spaces | cs.CV | In this paper we are interested in analyzing behaviour in crowded public
places at the level of holistic motion. Our aim is to learn, without user
input, strong scene priors or labelled data, the scope of "normal behaviour"
for a particular scene and thus alert to novelty in unseen footage. The first
contribution is a low-level motion model based on what we term tracklet
primitives, which are scene-specific elementary motions. We propose a
clustering-based algorithm for tracklet estimation from local approximations to
tracks of appearance features. This is followed by two methods for motion
novelty inference from tracklet primitives: (a) we describe an approach based
on a non-hierarchial ensemble of Markov chains as a means of capturing
behavioural characteristics at different scales, and (b) a more flexible
alternative which exhibits a higher generalizing power by accounting for
constraints introduced by intentionality and goal-oriented planning of human
motion in a particular scene. Evaluated on a 2h long video of a busy city
marketplace, both algorithms are shown to be successful at inferring unusual
behaviour, the latter model achieving better performance for novelties at a
larger spatial scale.
|
1309.6391 | Multiple-object tracking in cluttered and crowded public spaces | cs.CV | This paper addresses the problem of tracking moving objects of variable
appearance in challenging scenes rich with features and texture. Reliable
tracking is of pivotal importance in surveillance applications. It is made
particularly difficult by the nature of objects encountered in such scenes:
these too change in appearance and scale, and are often articulated (e.g.
humans). We propose a method which uses fast motion detection and segmentation
as a constraint for both building appearance models and their robust
propagation (matching) in time. The appearance model is based on sets of local
appearances automatically clustered using spatio-kinetic similarity, and is
updated with each new appearance seen. This integration of all seen appearances
of a tracked object makes it extremely resilient to errors caused by occlusion
and the lack of permanence of due to low data quality, appearance change or
background clutter. These theoretical strengths of our algorithm are
empirically demonstrated on two hour long video footage of a busy city
marketplace.
|
1309.6395 | Optimal Selection of Spectrum Sensing Duration for an Energy Harvesting
Cognitive Radio | cs.NI cs.IT math.IT | In this paper, we consider a time-slotted cognitive radio (CR) setting with
buffered and energy harvesting primary and CR users. At the beginning of each
time slot, the CR user probabilistically chooses the spectrum sensing duration
from a predefined set. If the primary user (PU) is sensed to be inactive, the
CR user accesses the channel immediately. The CR user optimizes the sensing
duration probabilities in order to maximize its mean data service rate with
constraints on the stability of the primary and cognitive queues. The
optimization problem is split into two subproblems. The first is a
linear-fractional program, and the other is a linear program. Both subproblems
can be solved efficiently.
|
1309.6433 | A Fuzzy expert system for goalkeeper quality recognition | cs.AI | Goalkeeper (GK) is an expert in soccer and goalkeeping is a complete
professional job. In fact, achieving success seems impossible without a
reliable GK. His effect in successes and failures is more dominant than other
players. The most visible mistakes in a game are those of goalkeeper's. In this
paper the expert fuzzy system is used as a suitable tool to study the quality
of a goalkeeper and compare it with others. Previously done researches are used
to find the goalkeepers' indexes in soccer. Soccer experts have found that a
successful GK should have some qualifications. A new pattern is offered here
which is called "Soccer goalkeeper quality recognition using fuzzy expert
systems". This pattern has some important capabilities. Firstly, among some
goalkeepers the one with the best quality for the main team arrange can be
chosen. Secondly, the need to expert coaches for choosing a GK using their
senses and experiences decreases a lot. Thirdly, in the survey of a GK,
quantitative criteria can be included, and finally this pattern is simple and
easy to understand.
|
1309.6449 | Exploring Programmable Self-Assembly in Non-DNA based Molecular
Computing | cs.CC cs.AI cs.CE physics.comp-ph physics.data-an | Self-assembly is a phenomenon observed in nature at all scales where
autonomous entities build complex structures, without external influences nor
centralised master plan. Modelling such entities and programming correct
interactions among them is crucial for controlling the manufacture of desired
complex structures at the molecular and supramolecular scale. This work focuses
on a programmability model for non DNA-based molecules and complex behaviour
analysis of their self-assembled conformations. In particular, we look into
modelling, programming and simulation of porphyrin molecules self-assembly and
apply Kolgomorov complexity-based techniques to classify and assess simulation
results in terms of information content. The analysis focuses on phase
transition, clustering, variability and parameter discovery which as a whole
pave the way to the notion of complex systems programmability.
|
1309.6455 | Maximizing the Spread of Stable Influence: Leveraging Norm-driven
Moral-Motivation for Green Behavior Change in Networks | cs.SI physics.soc-ph | In an effort to understand why individuals choose to participate in
personally-expensive pro-environmental behaviors, environmental and behavioral
economists have examined a moral-motivation model in which the decision to
adopt a pro-environmental behavior depends on the society-wide market share of
that behavior. An increasing body of practical research on adoption of
pro-environmental behavior emphasizes the importance of encouragement from
local social contacts and messaging about locally-embraced norms: we respond by
extending the moral-motivation model to a social networks setting. We obtain a
new decision rule: an individual adopts a pro-environmental behavior if he or
she observes a certain threshold of adoption within their local social
neighborhood. This gives rise to a concurrent update process which describes
adoption of a pro-environmental behavior spreading through a network. The
original moral-motivation model corresponds to the special case of our network
version in a complete graph.
By improving convergence results, we formulate modest-size Integer Programs
that accurately (but not efficiently) find minimum-size sets of nodes that
convert the entire network, or alternately that maximize long-term adoption in
the network given a limited number of nodes which may be temporarily converted.
Issues of stability in determining long-term adoption are key. We give hardness
of approximation results for these optimization problems. We demonstrate that
there exist classes of networks which qualitatively have severely different
behavior than the non-networked version, and provide preliminary computational
results in in modestly-sized highly-clustered small-world networks related to
the famous small-world networks of Watts and Strogatz.
|
1309.6484 | Capacity-aware back-pressure traffic signal control | cs.SY | The control of a network of signalized intersections is considered. Previous
work demonstrates that the so-called back-pressure control provides stability
guarantees, assuming infinite queues capacities. In this paper, we highlight
the failing of current back-pressure control under finite capacities by
identifying sources of non work-conservation and congestion propagation. We
propose the use of a normalized pressure which guarantees work conservation and
mitigates congestion propagation, while ensuring fairness at low traffic
densities, and recovering original back-pressure as capacities grow to
infinity. This capacity-aware back-pressure control allows to improve
performance as congestion increases, as indicated by simulation results, and
keeps the key benefits of back-pressure: ability to be distributed over
intersections and O(1) complexity.
|
1309.6487 | A Unified Framework for Representation-based Subspace Clustering of
Out-of-sample and Large-scale Data | cs.LG cs.CV stat.ML | Under the framework of spectral clustering, the key of subspace clustering is
building a similarity graph which describes the neighborhood relations among
data points. Some recent works build the graph using sparse, low-rank, and
$\ell_2$-norm-based representation, and have achieved state-of-the-art
performance. However, these methods have suffered from the following two
limitations. First, the time complexities of these methods are at least
proportional to the cube of the data size, which make those methods inefficient
for solving large-scale problems. Second, they cannot cope with out-of-sample
data that are not used to construct the similarity graph. To cluster each
out-of-sample datum, the methods have to recalculate the similarity graph and
the cluster membership of the whole data set. In this paper, we propose a
unified framework which makes representation-based subspace clustering
algorithms feasible to cluster both out-of-sample and large-scale data. Under
our framework, the large-scale problem is tackled by converting it as
out-of-sample problem in the manner of "sampling, clustering, coding, and
classifying". Furthermore, we give an estimation for the error bounds by
treating each subspace as a point in a hyperspace. Extensive experimental
results on various benchmark data sets show that our methods outperform several
recently-proposed scalable methods in clustering large-scale data set.
|
1309.6527 | Describing Papers and Reviewers' Competences by Taxonomy of Keywords | cs.IR cs.DL | This article focuses on the importance of the precise calculation of
similarity factors between papers and reviewers for performing a fair and
accurate automatic assignment of reviewers to papers. It suggests that papers
and reviewers' competences should be described by taxonomy of keywords so that
the implied hierarchical structure allows similarity measures to take into
account not only the number of exactly matching keywords, but in case of
non-matching ones to calculate how semantically close they are. The paper also
suggests a similarity measure derived from the well-known and widely-used
Dice's coefficient, but adapted in a way it could be also applied between sets
whose elements are semantically related to each other (as concepts in taxonomy
are). It allows a non-zero similarity factor to be accurately calculated
between a paper and a reviewer even if they do not share any keyword in common.
|
1309.6545 | Distinguishing Infections on Different Graph Topologies | cs.SI q-bio.PE | The history of infections and epidemics holds famous examples where
understanding, containing and ultimately treating an outbreak began with
understanding its mode of spread. Influenza, HIV and most computer viruses,
spread person to person, device to device, through contact networks; Cholera,
Cancer, and seasonal allergies, on the other hand, do not. In this paper we
study two fundamental questions of detection: first, given a snapshot view of a
(perhaps vanishingly small) fraction of those infected, under what conditions
is an epidemic spreading via contact (e.g., Influenza), distinguishable from a
"random illness" operating independently of any contact network (e.g., seasonal
allergies); second, if we do have an epidemic, under what conditions is it
possible to determine which network of interactions is the main cause of the
spread -- the causative network -- without any knowledge of the epidemic, other
than the identity of a minuscule subsample of infected nodes?
The core, therefore, of this paper, is to obtain an understanding of the
diagnostic power of network information. We derive sufficient conditions
networks must satisfy for these problems to be identifiable, and produce
efficient, highly scalable algorithms that solve these problems. We show that
the identifiability condition we give is fairly mild, and in particular, is
satisfied by two common graph topologies: the grid, and the Erdos-Renyi graphs.
|
1309.6550 | Loop Calculus for Non-Binary Alphabets using Concepts from Information
Geometry | cs.IT cond-mat.stat-mech math.IT | The Bethe approximation is a well-known approximation of the partition
function used in statistical physics. Recently, an equality relating the
partition function and its Bethe approximation was obtained for graphical
models with binary variables by Chertkov and Chernyak. In this equality, the
multiplicative error in the Bethe approximation is represented as a weighted
sum over all generalized loops in the graphical model. In this paper, the
equality is generalized to graphical models with non-binary alphabet using
concepts from information geometry.
|
1309.6584 | Should I Stay or Should I Go: Coordinating Biological Needs with
Continuously-updated Assessments of the Environment | cs.NE cs.LG q-bio.NC | This paper presents Wanderer, a model of how autonomous adaptive systems
coordinate internal biological needs with moment-by-moment assessments of the
probabilities of events in the external world. The extent to which Wanderer
moves about or explores its environment reflects the relative activations of
two competing motivational sub-systems: one represents the need to acquire
energy and it excites exploration, and the other represents the need to avoid
predators and it inhibits exploration. The environment contains food,
predators, and neutral stimuli. Wanderer responds to these events in a way that
is adaptive in the short turn, and reassesses the probabilities of these events
so that it can modify its long term behaviour appropriately. When food appears,
Wanderer be-comes satiated and exploration temporarily decreases. When a
predator appears, Wanderer both decreases exploration in the short term, and
becomes more "cautious" about exploring in the future. Wanderer also forms
associations between neutral features and salient ones (food and predators)
when they are present at the same time, and uses these associations to guide
its behaviour.
|
1309.6603 | The Random Bit Complexity of Mobile Robots Scattering | cs.DS cs.CC cs.DC cs.MA cs.RO | We consider the problem of scattering $n$ robots in a two dimensional
continuous space. As this problem is impossible to solve in a deterministic
manner, all solutions must be probabilistic. We investigate the amount of
randomness (that is, the number of random bits used by the robots) that is
required to achieve scattering. We first prove that $n \log n$ random bits are
necessary to scatter $n$ robots in any setting. Also, we give a sufficient
condition for a scattering algorithm to be random bit optimal. As it turns out
that previous solutions for scattering satisfy our condition, they are hence
proved random bit optimal for the scattering problem. Then, we investigate the
time complexity of scattering when strong multiplicity detection is not
available. We prove that such algorithms cannot converge in constant time in
the general case and in $o(\log \log n)$ rounds for random bits optimal
scattering algorithms. However, we present a family of scattering algorithms
that converge as fast as needed without using multiplicity detection. Also, we
put forward a specific protocol of this family that is random bit optimal ($n
\log n$ random bits are used) and time optimal ($\log \log n$ rounds are used).
This improves the time complexity of previous results in the same setting by a
$\log n$ factor. Aside from characterizing the random bit complexity of mobile
robot scattering, our study also closes its time complexity gap with and
without strong multiplicity detection (that is, $O(1)$ time complexity is only
achievable when strong multiplicity detection is available, and it is possible
to approach it as needed otherwise).
|
1309.6608 | Assessment of OpenStreetMap Data - A Review | cs.CY cs.DB cs.SI | The meaning and purposes of web has been changing and evolving day by day.
Web 2. 0 encouraged more contribution by the end users. This movement provided
revolutionary methods of sharing and computing data by crowdsourcing such as
OpenStreetmap, also called "the wikification of maps" by some researchers. When
crowdsourcing collects huge data with help of general public with varying level
of mapping experience, the focus of researcher should be on analysing the data
rather than collecting it. Researchers have assessed the quality of
OpenStreetMap data by comparing it with proprietary data or data of
governmental map agencies. This study reviews the research work for assessment
of Open- StreetMap Data and also discusses about the future directions.
|
1309.6613 | Continuous-time Proportional-Integral Distributed Optimization for
Networked Systems | cs.SY math.OC | In this paper we explore the relationship between dual decomposition and the
consensus-based method for distributed optimization. The relationship is
developed by examining the similarities between the two approaches and their
relationship to gradient-based constrained optimization. By formulating each
algorithm in continuous-time, it is seen that both approaches use a gradient
method for optimization with one using a proportional control term and the
other using an integral control term to drive the system to the constraint set.
Therefore, a significant contribution of this paper is to combine these methods
to develop a continuous-time proportional-integral distributed optimization
method. Furthermore, we establish convergence using Lyapunov stability
techniques and utilizing properties from the network structure of the
multi-agent system.
|
1309.6627 | A Unified Filter for Simultaneous Input and State Estimation of Linear
Discrete-time Stochastic Systems | math.OC cs.SY math.DS | In this paper, we present a unified optimal and exponentially stable filter
for linear discrete-time stochastic systems that simultaneously estimates the
states and unknown inputs in an unbiased minimum-variance sense, without making
any assumptions on the direct feedthrough matrix. We also derive input and
state observability/detectability conditions, and analyze their connection to
the convergence and stability of the estimator. We discuss two variations of
the filter and their optimality and stability properties, and show that filters
in the literature, including the Kalman filter, are special cases of the filter
derived in this paper. Finally, illustrative examples are given to demonstrate
the performance of the unified unbiased minimum-variance filter.
|
1309.6629 | Stability of the Centrality of Unions of Networks on the Same Vertex Set | nlin.AO cs.SI math.CO | Let $G^1(V,E_1)$ and $G^2(V,E_2)$ be two networks on the same vertex set $V$
and consider the union of edges $G(V, E_1 \cup E_2)$. This paper studies the
stability of the Degree, Betweenness and Eigenvector Centrality of the
resultant network, $G(V, E_1 \cup E_2)$. Specifically assume $v^1_{max}$ and
$v^c_{max}$ are the highest centrality vertices of $G^1(V,E_1)$ and $G(V, E_1
\cup E_2)$ respectively, we want to find $Pr(v^1_{max} = v^c_{max})$.
|
1309.6650 | An Inter-lingual Reference Approach For Multi-Lingual Ontology Matching | cs.CL cs.DL | Ontologies are considered as the backbone of the Semantic Web. With the
rising success of the Semantic Web, the number of participating communities
from different countries is constantly increasing. The growing number of
ontologies available in different natural languages leads to an
interoperability problem. In this paper, we discuss several approaches for
ontology matching; examine similarities and differences, identify weaknesses,
and compare the existing automated approaches with the manual approaches for
integrating multilingual ontologies. In addition to that, we propose a new
architecture for a multilingual ontology matching service. As a case study we
used an example of two multilingual enterprise ontologies - the university
ontology of Freie Universitaet Berlin and the ontology for Fayoum University in
Egypt.
|
1309.6683 | Dynamic Structural Equation Models for Social Network Topology Inference | cs.SI | Many real-world processes evolve in cascades over complex networks, whose
topologies are often unobservable and change over time. However, the so-termed
adoption times when blogs mention popular news items, individuals in a
community catch an infectious disease, or consumers adopt a trendy electronics
product are typically known, and are implicitly dependent on the underlying
network. To infer the network topology, a \textit{dynamic} structural equation
model is adopted to capture the relationship between observed adoption times
and the unknown edge weights. Assuming a slowly time-varying topology and
leveraging the sparse connectivity inherent to social networks, edge weights
are estimated by minimizing a sparsity-regularized exponentially-weighted
least-squares criterion. To this end, solvers with complementary strengths are
developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient
iterations, the improved convergence rate of accelerated variants, or reduced
computational complexity of stochastic gradient descent. Numerical tests with
both synthetic and real data demonstrate the effectiveness of the novel
algorithms in unveiling sparse dynamically-evolving topologies, while
accounting for external influences in the adoption times. Key events in the
recent succession of political leadership in North Korea, explain connectivity
changes observed in the associated network inferred from global cascades of
online media.
|
1309.6687 | Removal of Data Incest in Multi-agent Social Learning in Social Networks | cs.SI physics.soc-ph | Motivated by online reputation systems, we investigate social learning in a
network where agents interact on a time dependent graph to estimate an
underlying state of nature. Agents record their own private observations, then
update their private beliefs about the state of nature using Bayes' rule. Based
on their belief, each agent then chooses an action (rating) from a finite set
and transmits this action over the social network. An important consequence of
such social learning over a network is the ruinous multiple re-use of
information known as data incest (or mis-information propagation). In this
paper, the data incest management problem in social learning context is
formulated on a directed acyclic graph. We give necessary and sufficient
conditions on the graph topology of social interactions to eliminate data
incest. A data incest removal algorithm is proposed such that the public belief
of social learning (and hence the actions of agents) is not affected by data
incest propagation. This results in an online reputation system with a higher
trust rating. Numerical examples are provided to illustrate the performance of
the proposed optimal data incest removal algorithm.
|
1309.6690 | Training-Based Synchronization and Channel Estimation in AF Two-Way
Relaying Networks | cs.IT math.IT | Two-way relaying networks (TWRNs) allow for more bandwidth efficient use of
the available spectrum since they allow for simultaneous information exchange
between two users with the assistance of an intermediate relay node. However,
due to superposition of signals at the relay node, the received signal at the
user terminals is affected by \emph{multiple impairments}, i.e., channel gains,
timing offsets, and carrier frequency offsets, that need to be jointly
estimated and compensated. This paper presents a training-based system model
for amplify-and-forward (AF) TWRNs in the presence of multiple impairments and
proposes maximum likelihood and differential evolution based algorithms for
joint estimation of these impairments. The Cramer-Rao lower bounds (CRLBs) for
the joint estimation of multiple impairments are derived. A minimum mean-square
error based receiver is then proposed to compensate the effect of multiple
impairments and decode each user's signal. Simulation results show that the
performance of the proposed estimators is very close to the derived CRLBs at
moderate-to-high signal-to-noise-ratios. It is also shown that the bit-error
rate performance of the overall AF TWRN is close to a TWRN that is based on
assumption of perfect knowledge of the synchronization parameters.
|
1309.6691 | Characterness: An Indicator of Text in the Wild | cs.CV | Text in an image provides vital information for interpreting its contents,
and text in a scene can aide with a variety of tasks from navigation, to
obstacle avoidance, and odometry. Despite its value, however, identifying
general text in images remains a challenging research problem. Motivated by the
need to consider the widely varying forms of natural text, we propose a
bottom-up approach to the problem which reflects the `characterness' of an
image region. In this sense our approach mirrors the move from saliency
detection methods to measures of `objectness'. In order to measure the
characterness we develop three novel cues that are tailored for character
detection, and a Bayesian method for their integration. Because text is made up
of sets of characters, we then design a Markov random field (MRF) model so as
to exploit the inherent dependencies between characters.
We experimentally demonstrate the effectiveness of our characterness cues as
well as the advantage of Bayesian multi-cue integration. The proposed text
detector outperforms state-of-the-art methods on a few benchmark scene text
detection datasets. We also show that our measurement of `characterness' is
superior than state-of-the-art saliency detection models when applied to the
same task.
|
1309.6693 | Improving Transient Performance of Adaptive Control Architectures using
Frequency-Limited System Error Dynamics | math.DS cs.SY | We develop an adaptive control architecture to achieve stabilization and
command following of uncertain dynamical systems with improved transient
performance. Our framework consists of a new reference system and an adaptive
controller. The proposed reference system captures a desired closed-loop
dynamical system behavior modified by a mismatch term representing the
high-frequency content between the uncertain dynamical system and this
reference system, i.e., the system error. In particular, this mismatch term
allows to limit the frequency content of the system error dynamics, which is
used to drive the adaptive controller. It is shown that this key feature of our
framework yields fast adaptation with- out incurring high-frequency
oscillations in the transient performance. We further show the effects of
design parameters on the system performance, analyze closeness of the uncertain
dynamical system to the unmodified (ideal) reference system, discuss robustness
of the proposed approach with respect to time-varying uncertainties and
disturbances, and make connections to gradient minimization and classical
control theory.
|
1309.6701 | Generalization of Rashmi-Shah-Kumar Minimum-Storage-Regenerating Codes | cs.IT math.IT | In this paper, we propose a generalized version of the Rashmi-Shah-Kumar
Minimum-Storage-Regenerating(RSK-MSR) codes based on the product-matrix
framework. For any $(n,k,d)$ such that $d \geq 2k-2$ and $d \leq n-1$, we can
directly construct an $(n,k,d)$ MSR code without constructing a larger MSR code
and shortening of the larger MSR code. As a result, the size of a finite field
over which the proposed code is defined is smaller than or equal to the size of
a finite field over which the RSK-MSR code is defined. In addition, the
$\{\ell,\ell'\}$ secure codes based on the generalized RSK-MSR codes can be
obtained by applying the construction method of $\{\ell,\ell'\}$ secure codes
proposed by Shah, Rashmi and Kumar. Furthermore, the message matrix of the
$(n,k,d)$ generalized RSK-MSR code is derived from that of the RSK-MSR code by
using the construction method of the $\{\ell=k,\ell'=0\}$ secure code.
|
1309.6707 | Distributed Online Learning in Social Recommender Systems | cs.SI cs.LG stat.ML | In this paper, we consider decentralized sequential decision making in
distributed online recommender systems, where items are recommended to users
based on their search query as well as their specific background including
history of bought items, gender and age, all of which comprise the context
information of the user. In contrast to centralized recommender systems, in
which there is a single centralized seller who has access to the complete
inventory of items as well as the complete record of sales and user
information, in decentralized recommender systems each seller/learner only has
access to the inventory of items and user information for its own products and
not the products and user information of other sellers, but can get commission
if it sells an item of another seller. Therefore the sellers must distributedly
find out for an incoming user which items to recommend (from the set of own
items or items of another seller), in order to maximize the revenue from own
sales and commissions. We formulate this problem as a cooperative contextual
bandit problem, analytically bound the performance of the sellers compared to
the best recommendation strategy given the complete realization of user
arrivals and the inventory of items, as well as the context-dependent purchase
probabilities of each item, and verify our results via numerical examples on a
distributed data set adapted based on Amazon data. We evaluate the dependence
of the performance of a seller on the inventory of items the seller has, the
number of connections it has with the other sellers, and the commissions which
the seller gets by selling items of other sellers to its users.
|
1309.6722 | Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern
Generation | cs.CL | This paper focuses on the automatic extraction of domain-specific sentiment
word (DSSW), which is a fundamental subtask of sentiment analysis. Most
previous work utilizes manual patterns for this task. However, the performance
of those methods highly relies on the labelled patterns or selected seeds. In
order to overcome the above problem, this paper presents an automatic framework
to detect large-scale domain-specific patterns for DSSW extraction. To this
end, sentiment seeds are extracted from massive dataset of user comments.
Subsequently, these sentiment seeds are expanded by synonyms using a
bootstrapping mechanism. Simultaneously, a synonymy graph is built and the
graph propagation algorithm is applied on the built synonymy graph. Afterwards,
syntactic and sequential relations between target words and high-ranked
sentiment words are extracted automatically to construct large-scale patterns,
which are further used to extracte DSSWs. The experimental results in three
domains reveal the effectiveness of our method.
|
1309.6727 | Genie Tree and Degrees of Freedom of the Symmetric MIMO Interfering
Broadcast Channel | cs.IT math.IT | In this paper, we study the information theoretic maximal degrees of freedom
(DoF) for the symmetric multi-input-multi-output (MIMO) interfering broadcast
channel (IBC) with arbitrary antenna configurations. For the G-cell K-user MXN
MIMO-IBC network, we find that the information theoretic maximal DoF per user
are related to three DoF bounds: 1) the decomposition DoF bound
d^{Decom}=MN/(M+KN), a lower-bound of linear interference alignment (IA) with
infinite time/frequency extensions (called asymptotic IA); 2) the proper DoF
bound d^{Proper}=(M+N)/(GK+1), an upper-bound of linear IA without
time/frequency extensions (called linear IA); and 3) the quantity DoF bound
d^{Quan}, a zigzag piecewise linear function of M and N. The whole region of
M/N can be divided into two regions, Region I and Region II. Specifically, for
most configurations in Region I, the information theoretic maximal DoF are the
decomposition DoF bound and can be achieved by the asymptotic IA. For all
configurations in Region II, the information theoretic maximal DoF are the
quantity DoF bound and can be achieved by the linear IA. To obtain the tight
upper-bound, we propose a unified way to construct genies, where the genies
help each base station or user resolve the maximal number of interference.
According to the feature that the designed genies with the same dimension can
derive identical DoF upper-bound, we convert the information theoretic DoF
upper-bound problem into a linear algebra problem and obtain the closed-form
DoF upper-bound expression. Moreover, we develop a non-iterative linear IA
transceiver to achieve the DoF upper-bound for the networks with antenna
configurations in Region II, which means that the derived DoF upper-bound is
tightest. The basic principles to derive the DoF upper-bound and design the
linear IA transceiver to achieve the DoF upper-bound can be extended into
general asymmetric networks.
|
1309.6732 | Outage Capacity of Opportunistic Beamforming with Random User Locations | cs.IT math.IT | This paper studies the outage capacity of a network consisting of a multitude
of heterogenous mobile users, and operating according to the classical
opportunistic beamforming framework. The base station is located at the center
of the cell, which is modeled as a disk of finite radius. The random user
locations are modeled using a homogenous spatial Poisson point process. The
received signals are impaired by both fading and location dependent path loss.
For this system, we first derive an expression for the beam outage probability.
This expression holds for all path loss models that satisfy some mild
conditions. Then, we focus on two specific path loss models (i.e., an unbounded
model and a more realistic bounded one) to illustrate the applications of our
results. In the large system limit where the cell radius tends to infinity, the
beam outage capacity and its scaling behavior are derived for the selected
specific path loss models. It is shown that the beam outage capacity scales
logarithmically for the unbounded model. On the other hand, this scaling
behavior becomes double logarithmic for the bounded model. Intuitive
explanations are provided as to why we observe different scaling behavior for
different path loss models. Numerical evaluations are performed to give further
insights, and to illustrate the applicability of the outage capacity results
even to a cell having a small finite radius.
|
1309.6740 | Detection of the elite structure in a virtual multiplex social system by
means of a generalized $K$-core | physics.soc-ph cs.SI physics.data-an | Elites are subgroups of individuals within a society that have the ability
and means to influence, lead, govern, and shape societies. Members of elites
are often well connected individuals, which enables them to impose their
influence to many and to quickly gather, process, and spread information. Here
we argue that elites are not only composed of highly connected individuals, but
also of intermediaries connecting hubs to form a cohesive and structured
elite-subgroup at the core of a social network. For this purpose we present a
generalization of the $K$-core algorithm that allows to identify a social core
that is composed of well-connected hubs together with their `connectors'. We
show the validity of the idea in the framework of a virtual world defined by a
massive multiplayer online game, on which we have complete information of
various social networks. Exploiting this multiplex structure, we find that the
hubs of the generalized $K$-core identify those individuals that are high
social performers in terms of a series of indicators that are available in the
game. In addition, using a combined strategy which involves the generalized
$K$-core and the recently introduced $M$-core, the elites of the different
'nations' present in the game are perfectly identified as modules of the
generalized $K$-core. Interesting sudden shifts in the composition of the elite
cores are observed at deep levels. We show that elite detection with the
traditional $K$-core is not possible in a reliable way. The proposed method
might be useful in a series of more general applications, such as community
detection.
|
1309.6786 | One-class Collaborative Filtering with Random Graphs: Annotated Version | stat.ML cs.LG | The bane of one-class collaborative filtering is interpreting and modelling
the latent signal from the missing class. In this paper we present a novel
Bayesian generative model for implicit collaborative filtering. It forms a core
component of the Xbox Live architecture, and unlike previous approaches,
delineates the odds of a user disliking an item from simply not considering it.
The latent signal is treated as an unobserved random graph connecting users
with items they might have encountered. We demonstrate how large-scale
distributed learning can be achieved through a combination of stochastic
gradient descent and mean field variational inference over random graph
samples. A fine-grained comparison is done against a state of the art baseline
on real world data.
|
1309.6788 | Successive Interference Cancellation in Heterogeneous Cellular Networks | cs.IT cs.NI math.IT | At present, operators address the explosive growth of mobile data demand by
densification of the cellular network so as to reduce the transmitter-receiver
distance and to achieve higher spectral efficiency. Due to such network
densification and the intense proliferation of wireless devices, modern
wireless networks are interference-limited, which motivates the use of
interference mitigation and coordination techniques. In this work, we develop a
statistical framework to evaluate the performance of multi-tier heterogeneous
networks with successive interference cancellation (SIC) capabilities,
accounting for the computational complexity of the cancellation scheme and
relevant network related parameters such as random location of the access
points (APs) and mobile users, and the characteristics of the wireless
propagation channel. We explicitly model the consecutive events of canceling
interferers and we derive the success probability to cancel the n-th strongest
signal and to decode the signal of interest after n cancellations. When users
are connected to the AP which provides the maximum average received signal
power, the analysis indicates that the performance gains of SIC diminish
quickly with n and the benefits are modest for realistic values of the
signal-to-interference ratio (SIR). We extend the statistical model to include
several association policies where distinct gains of SIC are expected: (i)
minimum load association, (ii) maxi- mum instantaneous SIR association, and
(iii) range expansion. Numerical results show the effectiveness of SIC for the
considered association policies. This work deepens the understanding of SIC by
defining the achievable gains for different association policies in multi-tier
heterogeneous networks.
|
1309.6806 | Blind pilot decontamination | cs.IT math.IT | A subspace projection to improve channel estimation in massive multi-antenna
systems is proposed and analyzed. Together with power-controlled hand-off, it
can mitigate the pilot contamination problem without the need for coordination
among cells. The proposed method is blind in the sense that it does not require
pilot data to find the appropriate subspace. It is based on the theory of large
random matrices that predicts that the eigenvalue spectra of large sample
covariance matrices can asymptotically decompose into disjoint bulks as the
matrix size grows large. Random matrix and free probability theory are utilized
to predict under which system parameters such a bulk decomposition takes place.
Simulation results are provided to confirm that the proposed method outperforms
conventional linear channel estimation if bulk separation occurs.
|
1309.6811 | Generative Multiple-Instance Learning Models For Quantitative
Electromyography | cs.LG stat.ML | We present a comprehensive study of the use of generative modeling approaches
for Multiple-Instance Learning (MIL) problems. In MIL a learner receives
training instances grouped together into bags with labels for the bags only
(which might not be correct for the comprised instances). Our work was
motivated by the task of facilitating the diagnosis of neuromuscular disorders
using sets of motor unit potential trains (MUPTs) detected within a muscle
which can be cast as a MIL problem. Our approach leads to a state-of-the-art
solution to the problem of muscle classification. By introducing and analyzing
generative models for MIL in a general framework and examining a variety of
model structures and components, our work also serves as a methodological guide
to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets
and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.
|
1309.6812 | The Bregman Variational Dual-Tree Framework | cs.LG stat.ML | Graph-based methods provide a powerful tool set for many non-parametric
frameworks in Machine Learning. In general, the memory and computational
complexity of these methods is quadratic in the number of examples in the data
which makes them quickly infeasible for moderate to large scale datasets. A
significant effort to find more efficient solutions to the problem has been
made in the literature. One of the state-of-the-art methods that has been
recently introduced is the Variational Dual-Tree (VDT) framework. Despite some
of its unique features, VDT is currently restricted only to Euclidean spaces
where the Euclidean distance quantifies the similarity. In this paper, we
extend the VDT framework beyond the Euclidean distance to more general Bregman
divergences that include the Euclidean distance as a special case. By
exploiting the properties of the general Bregman divergence, we show how the
new framework can maintain all the pivotal features of the VDT framework and
yet significantly improve its performance in non-Euclidean domains. We apply
the proposed framework to different text categorization problems and
demonstrate its benefits over the original VDT.
|
1309.6813 | Hinge-loss Markov Random Fields: Convex Inference for Structured
Prediction | cs.LG stat.ML | Graphical models for structured domains are powerful tools, but the
computational complexities of combinatorial prediction spaces can force
restrictions on models, or require approximate inference in order to be
tractable. Instead of working in a combinatorial space, we use hinge-loss
Markov random fields (HL-MRFs), an expressive class of graphical models with
log-concave density functions over continuous variables, which can represent
confidences in discrete predictions. This paper demonstrates that HL-MRFs are
general tools for fast and accurate structured prediction. We introduce the
first inference algorithm that is both scalable and applicable to the full
class of HL-MRFs, and show how to train HL-MRFs with several learning
algorithms. Our experiments show that HL-MRFs match or surpass the predictive
performance of state-of-the-art methods, including discrete models, in four
application domains.
|
1309.6814 | High-dimensional Joint Sparsity Random Effects Model for Multi-task
Learning | cs.LG stat.ML | Joint sparsity regularization in multi-task learning has attracted much
attention in recent years. The traditional convex formulation employs the group
Lasso relaxation to achieve joint sparsity across tasks. Although this approach
leads to a simple convex formulation, it suffers from several issues due to the
looseness of the relaxation. To remedy this problem, we view jointly sparse
multi-task learning as a specialized random effects model, and derive a convex
relaxation approach that involves two steps. The first step learns the
covariance matrix of the coefficients using a convex formulation which we refer
to as sparse covariance coding; the second step solves a ridge regression
problem with a sparse quadratic regularizer based on the covariance matrix
obtained in the first step. It is shown that this approach produces an
asymptotically optimal quadratic regularizer in the multitask learning setting
when the number of tasks approaches infinity. Experimental results demonstrate
that the convex formulation obtained via the proposed model significantly
outperforms group Lasso (and related multi-stage formulations
|
1309.6815 | Lower Bounds for Exact Model Counting and Applications in Probabilistic
Databases | cs.DB cs.AI cs.CC | The best current methods for exactly computing the number of satisfying
assignments, or the satisfying probability, of Boolean formulas can be seen,
either directly or indirectly, as building 'decision-DNNF' (decision
decomposable negation normal form) representations of the input Boolean
formulas. Decision-DNNFs are a special case of 'd-DNNF's where 'd' stands for
'deterministic'. We show that any decision-DNNF can be converted into an
equivalent 'FBDD' (free binary decision diagram) -- also known as a 'read-once
branching program' (ROBP or 1-BP) -- with only a quasipolynomial increase in
representation size in general, and with only a polynomial increase in size in
the special case of monotone k-DNF formulas. Leveraging known exponential lower
bounds for FBDDs, we then obtain similar exponential lower bounds for
decision-DNNFs which provide lower bounds for the recent algorithms. We also
separate the power of decision-DNNFs from d-DNNFs and a generalization of
decision-DNNFs known as AND-FBDDs. Finally we show how these imply exponential
lower bounds for natural problems associated with probabilistic databases.
|
1309.6816 | Reasoning about Probabilities in Dynamic Systems using Goal Regression | cs.AI | Reasoning about degrees of belief in uncertain dynamic worlds is fundamental
to many applications, such as robotics and planning, where actions modify state
properties and sensors provide measurements, both of which are prone to noise.
With the exception of limited cases such as Gaussian processes over linear
phenomena, belief state evolution can be complex and hard to reason with in a
general way. This paper proposes a framework with new results that allows the
reduction of subjective probabilities after sensing and acting to questions
about the initial state only. We build on an expressive probabilistic
first-order logical account by Bacchus, Halpern and Levesque, resulting in a
methodology that, in principle, can be coupled with a variety of existing
inference solutions.
|
1309.6817 | Probabilistic Conditional Preference Networks | cs.AI | In order to represent the preferences of a group of individuals, we introduce
Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for
representing probability distributions over preference orderings. We argue that
they are useful for aggregating preferences or modelling noisy preferences.
Then we give efficient algorithms for the main reasoning problems, namely for
computing the probability that a given outcome is preferred to another one, and
the probability that a given outcome is optimal. As a by-product, we obtain an
unexpected linear-time algorithm for checking dominance in a standard,
tree-structured CP-net.
|
1309.6818 | Boosting in the presence of label noise | cs.LG stat.ML | Boosting is known to be sensitive to label noise. We studied two approaches
to improve AdaBoost's robustness against labelling errors. One is to employ a
label-noise robust classifier as a base learner, while the other is to modify
the AdaBoost algorithm to be more robust. Empirical evaluation shows that a
committee of robust classifiers, although converges faster than non label-noise
aware AdaBoost, is still susceptible to label noise. However, pairing it with
the new robust Boosting algorithm we propose here results in a more resilient
algorithm under mislabelling.
|
1309.6819 | Hilbert Space Embeddings of Predictive State Representations | cs.LG stat.ML | Predictive State Representations (PSRs) are an expressive class of models for
controlled stochastic processes. PSRs represent state as a set of predictions
of future observable events. Because PSRs are defined entirely in terms of
observable data, statistically consistent estimates of PSR parameters can be
learned efficiently by manipulating moments of observed training data. Most
learning algorithms for PSRs have assumed that actions and observations are
finite with low cardinality. In this paper, we generalize PSRs to infinite sets
of observations and actions, using the recent concept of Hilbert space
embeddings of distributions. The essence is to represent the state as a
nonparametric conditional embedding operator in a Reproducing Kernel Hilbert
Space (RKHS) and leverage recent work in kernel methods to estimate, predict,
and update the representation. We show that these Hilbert space embeddings of
PSRs are able to gracefully handle continuous actions and observations, and
that our learned models outperform competing system identification algorithms
on several prediction benchmarks.
|
1309.6820 | SparsityBoost: A New Scoring Function for Learning Bayesian Network
Structure | cs.LG cs.AI stat.ML | We give a new consistent scoring function for structure learning of Bayesian
networks. In contrast to traditional approaches to scorebased structure
learning, such as BDeu or MDL, the complexity penalty that we propose is
data-dependent and is given by the probability that a conditional independence
test correctly shows that an edge cannot exist. What really distinguishes this
new scoring function from earlier work is that it has the property of becoming
computationally easier to maximize as the amount of data increases. We prove a
polynomial sample complexity result, showing that maximizing this score is
guaranteed to correctly learn a structure with no false edges and a
distribution close to the generating distribution, whenever there exists a
Bayesian network which is a perfect map for the data generating distribution.
Although the new score can be used with any search algorithm, we give empirical
results showing that it is particularly effective when used together with a
linear programming relaxation approach to Bayesian network structure learning.
|
1309.6821 | Sample Complexity of Multi-task Reinforcement Learning | cs.LG stat.ML | Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this paper, we introduce a new multi-task algorithm for a sequence
of reinforcement-learning tasks when each task is sampled independently from
(an unknown) distribution over a finite set of Markov decision processes whose
parameters are initially unknown. For this setting, we prove under certain
assumptions that the per-task sample complexity of exploration is reduced
significantly due to transfer compared to standard single-task algorithms. Our
multi-task algorithm also has the desired characteristic that it is guaranteed
not to exhibit negative transfer: in the worst case its per-task sample
complexity is comparable to the corresponding single-task algorithm.
|
1309.6822 | Automorphism Groups of Graphical Models and Lifted Variational Inference | cs.AI | Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general exponential family. Its group action partitions the
set of random variables and feature functions into equivalent classes (called
orbits) having identical marginals and expectations. Then the inference problem
is effectively reduced to that of computing marginals or expectations for each
class, thus avoiding the need to deal with each individual variable or feature.
We demonstrate the usefulness of this general framework in lifting two classes
of variational approximation for maximum a posteriori (MAP) inference: local
linear programming (LP) relaxation and local LP relaxation with cycle
constraints; the latter yields the first lifted variational inference algorithm
that operates on a bound tighter than the local constraints.
|
1309.6823 | Convex Relaxations of Bregman Divergence Clustering | cs.LG stat.ML | Although many convex relaxations of clustering have been proposed in the past
decade, current formulations remain restricted to spherical Gaussian or
discriminative models and are susceptible to imbalanced clusters. To address
these shortcomings, we propose a new class of convex relaxations that can be
flexibly applied to more general forms of Bregman divergence clustering. By
basing these new formulations on normalized equivalence relations we retain
additional control on relaxation quality, which allows improvement in
clustering quality. We furthermore develop optimization methods that improve
scalability by exploiting recent implicit matrix norm methods. In practice, we
find that the new formulations are able to efficiently produce tighter
clusterings that improve the accuracy of state of the art methods.
|
1309.6824 | Learning Sparse Causal Models is not NP-hard | cs.AI | This paper shows that causal model discovery is not an NP-hard problem, in
the sense that for sparse graphs bounded by node degree k the sound and
complete causal model can be obtained in worst case order N^{2(k+2)}
independence tests, even when latent variables and selection bias may be
present. We present a modification of the well-known FCI algorithm that
implements the method for an independence oracle, and suggest improvements for
sample/real-world data versions. It does not contradict any known hardness
results, and does not solve an NP-hard problem: it just proves that sparse
causal discovery is perhaps more complicated, but not as hard as learning
minimal Bayesian networks.
|
1309.6825 | Advances in Bayesian Network Learning using Integer Programming | cs.AI | We consider the problem of learning Bayesian networks (BNs) from complete
discrete data. This problem of discrete optimisation is formulated as an
integer program (IP). We describe the various steps we have taken to allow
efficient solving of this IP. These are (i) efficient search for cutting
planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal)
BNs and (iii) tightening the linear relaxation of the IP. After relating this
BN learning problem to set covering and the multidimensional 0-1 knapsack
problem, we present our empirical results. These show improvements, sometimes
dramatic, over earlier results.
|
1309.6826 | Qualitative Possibilistic Mixed-Observable MDPs | cs.AI | Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs
used to model situations where the agent's initial belief or observation
probabilities are imprecise due to lack of past experiences or insufficient
data collection. However, like probabilistic POMDPs, optimally solving
pi-POMDPs is intractable: the finite belief state space exponentially grows
with the number of system's states. In this paper, a possibilistic version of
Mixed-Observable MDPs is presented to get around this issue: the complexity of
solving pi-POMDPs, some state variables of which are fully observable, can be
then dramatically reduced. A value iteration algorithm for this new formulation
under infinite horizon is next proposed and the optimality of the returned
policy (for a specified criterion) is shown assuming the existence of a "stay"
action in some goal states. Experimental work finally shows that this
possibilistic model outperforms probabilistic POMDPs commonly used in robotics,
for a target recognition problem where the agent's observations are imprecise.
|
1309.6827 | Optimization With Parity Constraints: From Binary Codes to Discrete
Integration | cs.AI | Many probabilistic inference tasks involve summations over exponentially
large sets. Recently, it has been shown that these problems can be reduced to
solving a polynomial number of MAP inference queries for a model augmented with
randomly generated parity constraints. By exploiting a connection with
max-likelihood decoding of binary codes, we show that these optimizations are
computationally hard. Inspired by iterative message passing decoding
algorithms, we propose an Integer Linear Programming (ILP) formulation for the
problem, enhanced with new sparsification techniques to improve decoding
performance. By solving the ILP through a sequence of LP relaxations, we get
both lower and upper bounds on the partition function, which hold with high
probability and are much tighter than those obtained with variational methods.
|
1309.6828 | Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical
Efficiency | cs.AI | Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such
as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably
good action, but provide rather poor worst-case guarantees on performance
improvement over time. In contrast, a recently introduced MCTS algorithm BRUE
guarantees exponential-rate improvement over time, yet it is not geared towards
identifying reasonably good choices right at the go. We take a stand on the
individual strengths of these two classes of algorithms, and show how they can
be effectively connected. We then rationalize a principle of "selective tree
expansion", and suggest a concrete implementation of this principle within
MCTS. The resulting algorithm,s favorably compete with other MCTS algorithms
under short planning times, while preserving the attractive convergence
properties of BRUE.
|
1309.6829 | Bethe-ADMM for Tree Decomposition based Parallel MAP Inference | cs.AI cs.LG stat.ML | We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based
proximal function, which makes each subproblem in ADMM easy to solve in
parallel using the sum-product algorithm. We rigorously prove global
convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on
both synthetic and real datasets to illustrate its effectiveness. Further, the
parallel Bethe-ADMM is shown to scale almost linearly with increasing number of
cores.
|
1309.6830 | Building Bridges: Viewing Active Learning from the Multi-Armed Bandit
Lens | cs.LG stat.ML | In this paper we propose a multi-armed bandit inspired, pool based active
learning algorithm for the problem of binary classification. By carefully
constructing an analogy between active learning and multi-armed bandits, we
utilize ideas such as lower confidence bounds, and self-concordant
regularization from the multi-armed bandit literature to design our proposed
algorithm. Our algorithm is a sequential algorithm, which in each round assigns
a sampling distribution on the pool, samples one point from this distribution,
and queries the oracle for the label of this sampled point. The design of this
sampling distribution is also inspired by the analogy between active learning
and multi-armed bandits. We show how to derive lower confidence bounds required
by our algorithm. Experimental comparisons to previously proposed active
learning algorithms show superior performance on some standard UCI datasets.
|
1309.6831 | Batch-iFDD for Representation Expansion in Large MDPs | cs.LG stat.ML | Matching pursuit (MP) methods are a promising class of feature construction
algorithms for value function approximation. Yet existing MP methods require
creating a pool of potential features, mandating expert knowledge or
enumeration of a large feature pool, both of which hinder scalability. This
paper introduces batch incremental feature dependency discovery (Batch-iFDD) as
an MP method that inherits a provable convergence property. Additionally,
Batch-iFDD does not require a large pool of features, leading to lower
computational complexity. Empirical policy evaluation results across three
domains with up to one million states highlight the scalability of Batch-iFDD
over the previous state of the art MP algorithm.
|
1309.6832 | Structured Message Passing | cs.AI | In this paper, we present structured message passing (SMP), a unifying
framework for approximate inference algorithms that take advantage of
structured representations such as algebraic decision diagrams and sparse hash
tables. These representations can yield significant time and space savings over
the conventional tabular representation when the message has several identical
values (context-specific independence) or zeros (determinism) or both in its
range. Therefore, in order to fully exploit the power of structured
representations, we propose to artificially introduce context-specific
independence and determinism in the messages. This yields a new class of
powerful approximate inference algorithms which includes popular algorithms
such as cluster-graph Belief propagation (BP), expectation propagation and
particle BP as special cases. We show that our new algorithms introduce several
interesting bias-variance trade-offs. We evaluate these trade-offs empirically
and demonstrate that our new algorithms are more accurate and scalable than
state-of-the-art techniques.
|
1309.6833 | Multiple Instance Learning by Discriminative Training of Markov Networks | cs.LG stat.ML | We introduce a graphical framework for multiple instance learning (MIL) based
on Markov networks. This framework can be used to model the traditional MIL
definition as well as more general MIL definitions. Different levels of
ambiguity -- the portion of positive instances in a bag -- can be explored in
weakly supervised data. To train these models, we propose a discriminative
max-margin learning algorithm leveraging efficient inference for
cardinality-based cliques. The efficacy of the proposed framework is evaluated
on a variety of data sets. Experimental results verify that encoding or
learning the degree of ambiguity can improve classification performance.
|
1309.6834 | Unsupervised Learning of Noisy-Or Bayesian Networks | cs.LG stat.ML | This paper considers the problem of learning the parameters in Bayesian
networks of discrete variables with known structure and hidden variables.
Previous approaches in these settings typically use expectation maximization;
when the network has high treewidth, the required expectations might be
approximated using Monte Carlo or variational methods. We show how to avoid
inference altogether during learning by giving a polynomial-time algorithm
based on the method-of-moments, building upon recent work on learning
discrete-valued mixture models. In particular, we show how to learn the
parameters for a family of bipartite noisy-or Bayesian networks. In our
experimental results, we demonstrate an application of our algorithm to
learning QMR-DT, a large Bayesian network used for medical diagnosis. We show
that it is possible to fully learn the parameters of QMR-DT even when only the
findings are observed in the training data (ground truth diseases unknown).
|
1309.6835 | Gaussian Processes for Big Data | cs.LG stat.ML | We introduce stochastic variational inference for Gaussian process models.
This enables the application of Gaussian process (GP) models to data sets
containing millions of data points. We show how GPs can be vari- ationally
decomposed to depend on a set of globally relevant inducing variables which
factorize the model in the necessary manner to perform variational inference.
Our ap- proach is readily extended to models with non-Gaussian likelihoods and
latent variable models based around Gaussian processes. We demonstrate the
approach on a simple toy problem and two real world data sets.
|
1309.6836 | Discovering Cyclic Causal Models with Latent Variables: A General
SAT-Based Procedure | cs.AI | We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns "unknown"
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.
|
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