pwc-archive/papers-with-abstracts huggingface dataset:
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
Authors: Anh Cat Le Ngo, Kenneth Li-Minn Ang, Guoping Qiu, Jasmine Kah-Phooi Seng
Date: 2013-01-31 00:00:00
Tasks: Binary Classification, General Classification

The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between centre and surround
classes. Discriminant power of features for the classification is measured as
mutual information between distributions of image features and corresponding
classes . As the estimated discrepancy very much depends on considered scale
level, multi-scale structure and discriminant power are integrated by employing
discrete wavelet features and Hidden Markov Tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, a saliency value for
each square block at each scale level is computed with discriminant power
principle. Finally, across multiple scales is integrated the final saliency map
by an information maximization rule. Both standard quantitative tools such as
NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed
multi-scale discriminant saliency (MDIS) method against the well-know
information based approach AIM on its released image collection with
eye-tracking data. Simulation results are presented and analysed to verify the
validity of MDIS as well as point out its limitation for further research
direction.
Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization
Authors: Cristina Garcia-Cardona, Arjuna Flenner, Allon G. Percus
Date: 2013-06-06 00:00:00
Tasks: Classification, General Classification

We present a graph-based variational algorithm for classification of
high-dimensional data, generalizing the binary diffuse interface model to the
case of multiple classes. Motivated by total variation techniques, the method
involves minimizing an energy functional made up of three terms. The first two
terms promote a stepwise continuous classification function with sharp
transitions between classes, while preserving symmetry among the class labels.
The third term is a data fidelity term, allowing us to incorporate prior
information into the model in a semi-supervised framework. The performance of
the algorithm on synthetic data, as well as on the COIL and MNIST benchmark
datasets, is competitive with state-of-the-art graph-based multiclass
segmentation methods.
Efficient learning of simplices
Authors: Joseph Anderson, Navin Goyal, Luis Rademacher
Date: 2012-11-09 00:00:00

We show an efficient algorithm for the following problem: Given uniformly
random points from an arbitrary n-dimensional simplex, estimate the simplex.
The size of the sample and the number of arithmetic operations of our algorithm
are polynomial in n. This answers a question of Frieze, Jerrum and Kannan
[FJK]. Our result can also be interpreted as efficiently learning the
intersection of n+1 half-spaces in R^n in the model where the intersection is
bounded and we are given polynomially many uniform samples from it. Our proof
uses the local search technique from Independent Component Analysis (ICA), also
used by [FJK]. Unlike these previous algorithms, which were based on analyzing
the fourth moment, ours is based on the third moment.
  We also show a direct connection between the problem of learning a simplex
and ICA: a simple randomized reduction to ICA from the problem of learning a
simplex. The connection is based on a known representation of the uniform
measure on a simplex. Similar representations lead to a reduction from the
problem of learning an affine transformation of an n-dimensional l_p ball to
ICA.
Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs
Authors: Michael Chertkov, Andrew Gelfand, Jinwoo Shin
Date: 2013-06-05 00:00:00

This manuscript discusses computation of the Partition Function (PF) and the
Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We
present two novel problem formulations - one for computing the PF of a Perfect
Matching (PM) and one for finding MWPMs - that build upon the inter-related
Bethe Free Energy, Belief Propagation (BP), Loop Calculus (LC), Integer Linear
Programming (ILP) and Linear Programming (LP) frameworks. First, we describe an
extension of the LC framework to the PM problem. The resulting formulas, coined
(fractional) Bootstrap-BP, express the PF of the original model via the BFE of
an alternative PM problem. We then study the zero-temperature version of this
Bootstrap-BP formula for approximately solving the MWPM problem. We do so by
leveraging the Bootstrap-BP formula to construct a sequence of MWPM problems,
where each new problem in the sequence is formed by contracting odd-sized
cycles (or blossoms) from the previous problem. This Bootstrap-and-Contract
procedure converges reliably and generates an empirically tight upper bound for
the MWPM. We conclude by discussing the relationship between our iterative
procedure and the famous Blossom Algorithm of Edmonds '65 and demonstrate the
performance of the Bootstrap-and-Contract approach on a variety of weighted PM
problems.
Multiclass Total Variation Clustering
Authors: Xavier Bresson, Thomas Laurent, David Uminsky, James H. von Brecht
Date: 2013-06-05 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering

Ideas from the image processing literature have recently motivated a new set
of clustering algorithms that rely on the concept of total variation. While
these algorithms perform well for bi-partitioning tasks, their recursive
extensions yield unimpressive results for multiclass clustering tasks. This
paper presents a general framework for multiclass total variation clustering
that does not rely on recursion. The results greatly outperform previous total
variation algorithms and compare well with state-of-the-art NMF approaches.
A hybrid bat algorithm
Authors: Iztok Fister Jr., Dušan Fister, Xin-She Yang
Date: 2013-03-25 00:00:00

Swarm intelligence is a very powerful technique to be used for optimization
purposes. In this paper we present a new swarm intelligence algorithm, based on
the bat algorithm. The Bat algorithm is hybridized with differential evolution
strategies. Besides showing very promising results of the standard benchmark
functions, this hybridization also significantly improves the original bat
algorithm.
Securing Your Transactions: Detecting Anomalous Patterns In XML Documents
Authors: Eitan Menahem, Alon Schclar, Lior Rokach, Yuval Elovici
Date: 2012-09-09 00:00:00
Tasks: Anomaly Detection

XML transactions are used in many information systems to store data and
interact with other systems. Abnormal transactions, the result of either an
on-going cyber attack or the actions of a benign user, can potentially harm the
interacting systems and therefore they are regarded as a threat. In this paper
we address the problem of anomaly detection and localization in XML
transactions using machine learning techniques. We present a new XML anomaly
detection framework, XML-AD. Within this framework, an automatic method for
extracting features from XML transactions was developed as well as a practical
method for transforming XML features into vectors of fixed dimensionality. With
these two methods in place, the XML-AD framework makes it possible to utilize
general learning algorithms for anomaly detection. Central to the functioning
of the framework is a novel multi-univariate anomaly detection algorithm,
ADIFA. The framework was evaluated on four XML transactions datasets, captured
from real information systems, in which it achieved over 89% true positive
detection rate with less than a 0.2% false positive rate.
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
Authors: Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
Date: 2013-06-05 00:00:00
Tasks: parameter estimation, Segmentation

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use
probabilistic segmentation methods. By combining contrast terms with prior
terms, it provides accurate segmentations of medical images in a fully
automated manner. However, one of the main drawbacks of using the RW algorithm
is that its parameters have to be hand-tuned. we propose a novel discriminative
learning framework that estimates the parameters using a training dataset. The
main challenge we face is that the training samples are not fully supervised.
Speci cally, they provide a hard segmentation of the images, instead of a
proba-bilistic segmentation. We overcome this challenge by treating the optimal
probabilistic segmentation that is compatible with the given hard segmentation
as a latent variable. This allows us to employ the latent support vector
machine formulation for parameter estimation. We show that our approach signi
cantly outperforms the baseline methods on a challenging dataset consisting of
real clinical 3D MRI volumes of skeletal muscles.
Fast Dual Variational Inference for Non-Conjugate LGMs
Authors: Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger
Date: 2013-06-05 00:00:00
Tasks: Bayesian Inference, Variational Inference

Latent Gaussian models (LGMs) are widely used in statistics and machine
learning. Bayesian inference in non-conjugate LGMs is difficult due to
intractable integrals involving the Gaussian prior and non-conjugate
likelihoods. Algorithms based on variational Gaussian (VG) approximations are
widely employed since they strike a favorable balance between accuracy,
generality, speed, and ease of use. However, the structure of the optimization
problems associated with these approximations remains poorly understood, and
standard solvers take too long to converge. We derive a novel dual variational
inference approach that exploits the convexity property of the VG
approximations. We obtain an algorithm that solves a convex optimization
problem, reduces the number of variational parameters, and converges much
faster than previous methods. Using real-world data, we demonstrate these
advantages on a variety of LGMs, including Gaussian process classification, and
latent Gaussian Markov random fields.
ROTUNDE - A Smart Meeting Cinematography Initiative: Tools, Datasets, and Benchmarks for Cognitive Interpretation and Control
Authors: Mehul Bhatt, Jakob Suchan, Christian Freksa
Date: 2013-06-05 00:00:00

We construe smart meeting cinematography with a focus on professional
situations such as meetings and seminars, possibly conducted in a distributed
manner across socio-spatially separated groups. The basic objective in smart
meeting cinematography is to interpret professional interactions involving
people, and automatically produce dynamic recordings of discussions, debates,
presentations etc in the presence of multiple communication modalities. Typical
modalities include gestures (e.g., raising one's hand for a question,
applause), voice and interruption, electronic apparatus (e.g., pressing a
button), movement (e.g., standing-up, moving around) etc. ROTUNDE, an instance
of smart meeting cinematography concept, aims to: (a) develop
functionality-driven benchmarks with respect to the interpretation and control
capabilities of human-cinematographers, real-time video editors, surveillance
personnel, and typical human performance in everyday situations; (b) Develop
general tools for the commonsense cognitive interpretation of dynamic scenes
from the viewpoint of visuo-spatial cognition centred perceptual
narrativisation. Particular emphasis is placed on declarative representations
and interfacing mechanisms that seamlessly integrate within large-scale
cognitive (interaction) systems and companion technologies consisting of
diverse AI sub-components. For instance, the envisaged tools would provide
general capabilities for high-level commonsense reasoning about space, events,
actions, change, and interaction.
Quaternion Fourier Transform on Quaternion Fields and Generalizations
Authors: Eckhard Hitzer
Date: 2013-06-05 00:00:00

We treat the quaternionic Fourier transform (QFT) applied to quaternion
fields and investigate QFT properties useful for applications. Different forms
of the QFT lead us to different Plancherel theorems. We relate the QFT
computation for quaternion fields to the QFT of real signals. We research the
general linear ($GL$) transformation behavior of the QFT with matrices,
Clifford geometric algebra and with examples. We finally arrive at wide-ranging
non-commutative multivector FT generalizations of the QFT. Examples given are
new volume-time and spacetime algebra Fourier transformations.
Alternating Decision trees for early diagnosis of dengue fever
Authors: M. Naresh Kumar
Date: 2013-05-31 00:00:00
Tasks: Management, Specificity

Dengue fever is a flu-like illness spread by the bite of an infected mosquito
which is fast emerging as a major health problem. Timely and cost effective
diagnosis using clinical and laboratory features would reduce the mortality
rates besides providing better grounds for clinical management and disease
surveillance. We wish to develop a robust and effective decision tree based
approach for predicting dengue disease. Our analysis is based on the clinical
characteristics and laboratory measurements of the diseased individuals. We
have developed and trained an alternating decision tree with boosting and
compared its performance with C4.5 algorithm for dengue disease diagnosis. Of
the 65 patient records a diagnosis establishes that 53 individuals have been
confirmed to have dengue fever. An alternating decision tree based algorithm
was able to differentiate the dengue fever using the clinical and laboratory
data with number of correctly classified instances as 89%, F-measure of 0.86
and receiver operator characteristics (ROC) of 0.826 as compared to C4.5 having
correctly classified instances as 78%,h F-measure of 0.738 and ROC of 0.617
respectively. Alternating decision tree based approach with boosting has been
able to predict dengue fever with a higher degree of accuracy than C4.5 based
decision tree using simple clinical and laboratory features. Further analysis
on larger data sets is required to improve the sensitivity and specificity of
the alternating decision trees.
Distributed Bayesian inference for consistent labeling of tracked objects in non-overlapping camera networks
Authors: Jiuqing Wan, Li Liu
Date: 2013-06-05 00:00:00
Tasks: Bayesian Inference

One of the fundamental requirements for visual surveillance using
non-overlapping camera networks is the correct labeling of tracked objects on
each camera in a consistent way,in the sense that the captured tracklets, or
observations in this paper, of the same object at different cameras should be
assigned with the same label. In this paper, we formulate this task as a
Bayesian inference problem and propose a distributed inference framework in
which the posterior distribution of labeling variable corresponding to each
observation, conditioned on all history appearance and spatio-temporal evidence
made in the whole networks, is calculated based solely on local information
processing on each camera and mutual information exchanging between neighboring
cameras. In our framework, the number of objects presenting in the monitored
region, i.e. the sampling space of labeling variables, does not need to be
specified beforehand. Instead, it can be determined automatically on the fly.
In addition, we make no assumption about the appearance distribution of a
single object, but use similarity scores between appearance pairs, given by
advanced object re-identification algorithm, as appearance likelihood for
inference. This feature makes our method very flexible and competitive when
observing condition undergoes large changes across camera views. To cope with
the problem of missing detection, which is critical for distributed inference,
we consider an enlarged neighborhood of each camera during inference and use a
mixture model to describe the higher order spatio-temporal constraints. The
robustness of the algorithm against missing detection is improved at the cost
of slightly increased computation and communication burden at each camera node.
Finally, we demonstrate the effectiveness of our method through experiments on
an indoor Office Building dataset and an outdoor Campus Garden dataset.
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
Authors: Been Kim, Caleb M. Chacha, Julie Shah
Date: 2013-06-05 00:00:00
Tasks: Disaster Response, Translation

We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then translates the agreed upon plan into machine instructions for the robots.
We present an algorithm that reduces this translation burden by inferring the
final plan from a processed form of the human team's planning conversation. Our
approach combines probabilistic generative modeling with logical plan
validation used to compute a highly structured prior over possible plans. This
hybrid approach enables us to overcome the challenge of performing inference
over the large solution space with only a small amount of noisy data from the
team planning session. We validate the algorithm through human subject
experimentation and show we are able to infer a human team's final plan with
83% accuracy on average. We also describe a robot demonstration in which two
people plan and execute a first-response collaborative task with a PR2 robot.
To the best of our knowledge, this is the first work that integrates a logical
planning technique within a generative model to perform plan inference.
Fast Reinforcement Learning for Energy-Efficient Wireless Communications
Authors: Nicholas Mastronarde, Mihaela van der Schaar
Date: 2010-09-29 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Stochastic Optimization

We consider the problem of energy-efficient point-to-point transmission of
delay-sensitive data (e.g. multimedia data) over a fading channel. Existing
research on this topic utilizes either physical-layer centric solutions, namely
power-control and adaptive modulation and coding (AMC), or system-level
solutions based on dynamic power management (DPM); however, there is currently
no rigorous and unified framework for simultaneously utilizing both
physical-layer centric and system-level techniques to achieve the minimum
possible energy consumption, under delay constraints, in the presence of
stochastic and a priori unknown traffic and channel conditions. In this report,
we propose such a framework. We formulate the stochastic optimization problem
as a Markov decision process (MDP) and solve it online using reinforcement
learning. The advantages of the proposed online method are that (i) it does not
require a priori knowledge of the traffic arrival and channel statistics to
determine the jointly optimal power-control, AMC, and DPM policies; (ii) it
exploits partial information about the system so that less information needs to
be learned than when using conventional reinforcement learning algorithms; and
(iii) it obviates the need for action exploration, which severely limits the
adaptation speed and run-time performance of conventional reinforcement
learning algorithms. Our results show that the proposed learning algorithms can
converge up to two orders of magnitude faster than a state-of-the-art learning
algorithm for physical layer power-control and up to three orders of magnitude
faster than conventional reinforcement learning algorithms.
Online Learning under Delayed Feedback
Authors: Pooria Joulani, András György, Csaba Szepesvári
Date: 2013-06-04 00:00:00

Online learning with delayed feedback has received increasing attention
recently due to its several applications in distributed, web-based learning
problems. In this paper we provide a systematic study of the topic, and analyze
the effect of delay on the regret of online learning algorithms. Somewhat
surprisingly, it turns out that delay increases the regret in a multiplicative
way in adversarial problems, and in an additive way in stochastic problems. We
give meta-algorithms that transform, in a black-box fashion, algorithms
developed for the non-delayed case into ones that can handle the presence of
delays in the feedback loop. Modifications of the well-known UCB algorithm are
also developed for the bandit problem with delayed feedback, with the advantage
over the meta-algorithms that they can be implemented with lower complexity.
$\propto$SVM for learning with label proportions
Authors: Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang
Date: 2013-06-04 00:00:00

We study the problem of learning with label proportions in which the training
data is provided in groups and only the proportion of each class in each group
is known. We propose a new method called proportion-SVM, or $\propto$SVM, which
explicitly models the latent unknown instance labels together with the known
group label proportions in a large-margin framework. Unlike the existing works,
our approach avoids making restrictive assumptions about the data. The
$\propto$SVM model leads to a non-convex integer programming problem. In order
to solve it efficiently, we propose two algorithms: one based on simple
alternating optimization and the other based on a convex relaxation. Extensive
experiments on standard datasets show that $\propto$SVM outperforms the
state-of-the-art, especially for larger group sizes.
Urban ozone concentration forecasting with artificial neural network in Corsica
Authors: Wani W. Tamas, Gilles Notton, Christophe Paoli, Cyril Voyant, Marie Laure Nivet, Aurélia Balu
Date: 2013-06-04 00:00:00
Tasks: Clustering

Atmospheric pollutants concentration forecasting is an important issue in air
quality monitoring. Qualitair Corse, the organization responsible for
monitoring air quality in Corsica (France) region, needs to develop a
short-term prediction model to lead its mission of information towards the
public. Various deterministic models exist for meso-scale or local forecasting,
but need powerful large variable sets, a good knowledge of atmospheric
processes, and can be inaccurate because of local climatical or geographical
particularities, as observed in Corsica, a mountainous island located in a
Mediterranean Sea. As a result, we focus in this study on statistical models,
and particularly Artificial Neural Networks (ANN) that have shown good results
in the prediction of ozone concentration at horizon h+1 with data measured
locally. The purpose of this study is to build a predictor to realize
predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to
anticipate pollution peak formation and to take appropriated prevention
measures. Specific meteorological conditions are known to lead to particular
pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN
models will be used, for meteorological conditions clustering and for
operational forecasting.