pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Human Body Shape and Posture Under Clothing
Authors: Stefanie Wuhrer, Leonid Pishchulin, Alan Brunton, Chang Shu, Jochen Lang
Date: 2013-12-17 00:00:00

Estimating the body shape and posture of a dressed human subject in motion
represented as a sequence of (possibly incomplete) 3D meshes is important for
virtual change rooms and security. To solve this problem, statistical shape
spaces encoding human body shape and posture variations are commonly used to
constrain the search space for the shape estimate. In this work, we propose a
novel method that uses a posture-invariant shape space to model body shape
variation combined with a skeleton-based deformation to model posture
variation. Our method can estimate the body shape and posture of both static
scans and motion sequences of dressed human body scans. In case of motion
sequences, our method takes advantage of motion cues to solve for a single body
shape estimate along with a sequence of posture estimates. We apply our
approach to both static scans and motion sequences and demonstrate that using
our method, higher fitting accuracy is achieved than when using a variant of
the popular SCAPE model as statistical model.
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module
Authors: Jinwei Xu, Jiankun Hu, Xiuping Jia
Date: 2014-06-26 00:00:00
Tasks: Dictionary Learning, Self-Learning, set matching

Latent fingerprint has the practical value to identify the suspects who have
unintentionally left a trace of fingerprint in the crime scenes. However,
designing a fully automated latent fingerprint matcher is a very challenging
task as it needs to address many challenging issues including the separation of
overlapping structured patterns over the partial and poor quality latent
fingerprint image, and finding a match against a large background database that
would have different resolutions. Currently there is no fully automated latent
fingerprint matcher available to the public and most literature reports have
utilized a specialized latent fingerprint matcher COTS3 which is not accessible
to the public. This will make it infeasible to assess and compare the relevant
research work which is vital for this research community. In this study, we
target to develop a fully automated latent matcher for adaptive detection of
the region of interest and robust matching of latent prints. Unlike the
manually conducted matching procedure, the proposed latent matcher can run like
a sealed black box without any manual intervention. This matcher consists of
the following two modules: (i) the dictionary learning-based region of interest
(ROI) segmentation scheme; and (ii) the genetic algorithm-based minutiae set
matching unit. Experimental results on NIST SD27 latent fingerprint database
demonstrates that the proposed matcher outperforms the currently public
state-of-art latent fingerprint matcher.
FrameNet Resource Grammar Library for GF
Authors: Normunds Gruzitis, Peteris Paikens, Guntis Barzdins
Date: 2014-06-26 00:00:00

In this paper we present an ongoing research investigating the possibility
and potential of integrating frame semantics, particularly FrameNet, in the
Grammatical Framework (GF) application grammar development. An important
component of GF is its Resource Grammar Library (RGL) that encapsulates the
low-level linguistic knowledge about morphology and syntax of currently more
than 20 languages facilitating rapid development of multilingual applications.
In the ideal case, porting a GF application grammar to a new language would
only require introducing the domain lexicon - translation equivalents that are
interlinked via common abstract terms. While it is possible for a highly
restricted CNL, developing and porting a less restricted CNL requires above
average linguistic knowledge about the particular language, and above average
GF experience. Specifying a lexicon is mostly straightforward in the case of
nouns (incl. multi-word units), however, verbs are the most complex category
(in terms of both inflectional paradigms and argument structure), and adding
them to a GF application grammar is not a straightforward task. In this paper
we are focusing on verbs, investigating the possibility of creating a
multilingual FrameNet-based GF library. We propose an extension to the current
RGL, allowing GF application developers to define clauses on the semantic
level, thus leaving the language-specific syntactic mapping to this extension.
We demonstrate our approach by reengineering the MOLTO Phrasebook application
grammar.
Overlapping Community Detection Optimization and Nash Equilibrium
Authors: Michel Crampes, Michel Plantié
Date: 2014-06-26 00:00:00
Tasks: Community Detection

Community detection using both graphs and social networks is the focus of
many algorithms. Recent methods aimed at optimizing the so-called modularity
function proceed by maximizing relations within communities while minimizing
inter-community relations.
  However, given the NP-completeness of the problem, these algorithms are
heuristics that do not guarantee an optimum. In this paper, we introduce a new
algorithm along with a function that takes an approximate solution and modifies
it in order to reach an optimum. This reassignment function is considered a
'potential function' and becomes a necessary condition to asserting that the
computed optimum is indeed a Nash Equilibrium. We also use this function to
simultaneously show partitioning and overlapping communities, two detection and
visualization modes of great value in revealing interesting features of a
social network. Our approach is successfully illustrated through several
experiments on either real unipartite, multipartite or directed graphs of
medium and large-sized datasets.
Online learning in MDPs with side information
Authors: Yasin Abbasi-Yadkori, Gergely Neu
Date: 2014-06-26 00:00:00
Tasks: Recommendation Systems

We study online learning of finite Markov decision process (MDP) problems
when a side information vector is available. The problem is motivated by
applications such as clinical trials, recommendation systems, etc. Such
applications have an episodic structure, where each episode corresponds to a
patient/customer. Our objective is to compete with the optimal dynamic policy
that can take side information into account.
  We propose a computationally efficient algorithm and show that its regret is
at most $O(\sqrt{T})$, where $T$ is the number of rounds. To best of our
knowledge, this is the first regret bound for this setting.
A factorization criterion for acyclic directed mixed graphs
Authors: Thomas S. Richardson
Date: 2014-06-26 00:00:00

Acyclic directed mixed graphs, also known as semi-Markov models represent the
conditional independence structure induced on an observed margin by a DAG model
with latent variables. In this paper we present a factorization criterion for
these models that is equivalent to the global Markov property given by (the
natural extension of) d-separation.
Simulation leagues: Analysis of competition formats
Authors: David Budden, Peter Wang, Oliver Obst, Mikhail Prokopenko
Date: 2014-03-17 00:00:00

The selection of an appropriate competition format is critical for both the
success and credibility of any competition, both real and simulated. In this
paper, the automated parallelism offered by the RoboCupSoccer 2D simulation
league is leveraged to conduct a 28,000 game round-robin between the top 8
teams from RoboCup 2012 and 2013. A proposed new competition format is found to
reduce variation from the resultant statistically significant team performance
rankings by 75% and 67%, when compared to the actual competition results from
RoboCup 2012 and 2013 respectively. These results are statistically validated
by generating 10,000 random tournaments for each of the three considered
formats and comparing the respective distributions of ranking discrepancy.
Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation
Authors: Seyed Mostafa Kia
Date: 2014-06-25 00:00:00
Tasks: Two-sample testing

Recent advances in statistical theory, together with advances in the
computational power of computers, provide alternative methods to do
mass-univariate hypothesis testing in which a large number of univariate tests,
can be properly used to compare MEEG data at a large number of time-frequency
points and scalp locations. One of the major problematic aspects of this kind
of mass-univariate analysis is due to high number of accomplished hypothesis
tests. Hence procedures that remove or alleviate the increased probability of
false discoveries are crucial for this type of analysis. Here, I propose a new
method for mass-univariate analysis of MEEG data based on cross-validation
scheme. In this method, I suggest a hierarchical classification procedure under
k-fold cross-validation to detect which sensors at which time-bin and which
frequency-bin contributes in discriminating between two different stimuli or
tasks. To achieve this goal, a new feature extraction method based on the
discrete cosine transform (DCT) employed to get maximum advantage of all three
data dimensions. Employing cross-validation and hierarchy architecture
alongside the DCT feature space makes this method more reliable and at the same
time enough sensitive to detect the narrow effects in brain activities.
Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in the Othello League
Authors: Wojciech Jaśkowski
Date: 2014-06-05 00:00:00
Tasks: Board Games, Position

N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The most popular method of placing n-tuples consists in randomly
generating a small number of long, snake-shaped board location sequences. In
comparison, we show that learning n-tuple networks is significantly more
effective if they involve a large number of systematically placed, short,
straight n-tuples. Moreover, we demonstrate that in order to obtain the best
performance and the steepest learning curve for Othello it is enough to use
n-tuples of size just 2, yielding a network consisting of only 288 weights. The
best such network evolved in this study has been evaluated in the online
Othello League, obtaining the performance of nearly 96% --- more than any other
player to date.
Learning the ergodic decomposition
Authors: Nabil Al-Najjar, Eran Shmaya
Date: 2014-06-25 00:00:00

A Bayesian agent learns about the structure of a stationary process from ob-
serving past outcomes. We prove that his predictions about the near future
become ap- proximately those he would have made if he knew the long run
empirical frequencies of the process.
Causality Networks
Authors: Ishanu Chattopadhyay
Date: 2014-06-25 00:00:00

While correlation measures are used to discern statistical relationships
between observed variables in almost all branches of data-driven scientific
inquiry, what we are really interested in is the existence of causal
dependence. Designing an efficient causality test, that may be carried out in
the absence of restrictive pre-suppositions on the underlying dynamical
structure of the data at hand, is non-trivial. Nevertheless, ability to
computationally infer statistical prima facie evidence of causal dependence may
yield a far more discriminative tool for data analysis compared to the
calculation of simple correlations. In the present work, we present a new
non-parametric test of Granger causality for quantized or symbolic data streams
generated by ergodic stationary sources. In contrast to state-of-art binary
tests, our approach makes precise and computes the degree of causal dependence
between data streams, without making any restrictive assumptions, linearity or
otherwise. Additionally, without any a priori imposition of specific dynamical
structure, we infer explicit generative models of causal cross-dependence,
which may be then used for prediction. These explicit models are represented as
generalized probabilistic automata, referred to crossed automata, and are shown
to be sufficient to capture a fairly general class of causal dependence. The
proposed algorithms are computationally efficient in the PAC sense; $i.e.$, we
find good models of cross-dependence with high probability, with polynomial
run-times and sample complexities. The theoretical results are applied to
weekly search-frequency data from Google Trends API for a chosen set of
socially "charged" keywords. The causality network inferred from this dataset
reveals, quite expectedly, the causal importance of certain keywords. It is
also illustrated that correlation analysis fails to gather such insight.
Active Learning and Best-Response Dynamics
Authors: Maria-Florina Balcan, Chris Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song
Date: 2014-06-25 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Active Learning, Denoising

We examine an important setting for engineered systems in which low-power
distributed sensors are each making highly noisy measurements of some unknown
target function. A center wants to accurately learn this function by querying a
small number of sensors, which ordinarily would be impossible due to the high
noise rate. The question we address is whether local communication among
sensors, together with natural best-response dynamics in an
appropriately-defined game, can denoise the system without destroying the true
signal and allow the center to succeed from only a small number of active
queries. By using techniques from game theory and empirical processes, we prove
positive (and negative) results on the denoising power of several natural
dynamics. We then show experimentally that when combined with recent agnostic
active learning algorithms, this process can achieve low error from very few
queries, performing substantially better than active or passive learning
without these denoising dynamics as well as passive learning with denoising.
When is it Better to Compare than to Score?
Authors: Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin Wainwright
Date: 2014-06-25 00:00:00

When eliciting judgements from humans for an unknown quantity, one often has
the choice of making direct-scoring (cardinal) or comparative (ordinal)
measurements. In this paper we study the relative merits of either choice,
providing empirical and theoretical guidelines for the selection of a
measurement scheme. We provide empirical evidence based on experiments on
Amazon Mechanical Turk that in a variety of tasks, (pairwise-comparative)
ordinal measurements have lower per sample noise and are typically faster to
elicit than cardinal ones. Ordinal measurements however typically provide less
information. We then consider the popular Thurstone and Bradley-Terry-Luce
(BTL) models for ordinal measurements and characterize the minimax error rates
for estimating the unknown quantity. We compare these minimax error rates to
those under cardinal measurement models and quantify for what noise levels
ordinal measurements are better. Finally, we revisit the data collected from
our experiments and show that fitting these models confirms this prediction:
for tasks where the noise in ordinal measurements is sufficiently low, the
ordinal approach results in smaller errors in the estimation.
Optical Flow on Evolving Surfaces with Space and Time Regularisation
Authors: Clemens Kirisits, Lukas F. Lang, Otmar Scherzer
Date: 2013-10-01 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

We extend the concept of optical flow with spatiotemporal regularisation to a
dynamic non-Euclidean setting. Optical flow is traditionally computed from a
sequence of flat images. The purpose of this paper is to introduce variational
motion estimation for images that are defined on an evolving surface.
Volumetric microscopy images depicting a live zebrafish embryo serve as both
biological motivation and test data.
Support vector machine classification of dimensionally reduced structural MRI images for dementia
Authors: V. A. Miller, S. Erlien, J. Piersol
Date: 2014-06-25 00:00:00
Tasks: General Classification, Image Segmentation, Semantic Segmentation

We classify very-mild to moderate dementia in patients (CDR ranging from 0 to
2) using a support vector machine classifier acting on dimensionally reduced
feature set derived from MRI brain scans of the 416 subjects available in the
OASIS-Brains dataset. We use image segmentation and principal component
analysis to reduce the dimensionality of the data. Our resulting feature set
contains 11 features for each subject. Performance of the classifiers is
evaluated using 10-fold cross-validation. Using linear and (gaussian) kernels,
we obtain a training classification accuracy of 86.4% (90.1%), test accuracy of
85.0% (85.7%), test precision of 68.7% (68.5%), test recall of 68.0% (74.0%),
and test Matthews correlation coefficient of 0.594 (0.616).
A hybrid formalism to parse Sign Languages
Authors: Rémi Dubot, Christophe Collet
Date: 2014-03-18 00:00:00

Sign Language (SL) linguistic is dependent on the expensive task of
annotating. Some automation is already available for low-level information (eg.
body part tracking) and the lexical level has shown significant progresses. The
syntactic level lacks annotated corpora as well as complete and consistent
models. This article presents a solution for the automatic annotation of SL
syntactic elements. It exposes a formalism able to represent both
constituency-based and dependency-based models. The first enable the
representation the structures one may want to annotate, the second aims at
fulfilling the holes of the first. A parser is presented and used to conduct
two experiments on the solution. One experiment is on a real corpus, the other
is on a synthetic corpus.
Multi Circle Detection on Images Using Artificial Bee Colony (ABC) Optimization
Authors: Erik Cuevas, Felipe Sencion-Echauri, Daniel Zaldivar, Marco Perez Cisneros
Date: 2014-06-25 00:00:00

Hough transform (HT) has been the most common method for circle detection,
exhibiting robustness, but adversely demanding considerable computational
effort and large memory requirements. Alternative approaches include heuristic
methods that employ iterative optimization procedures for detecting multiple
circles. Since only one circle can be marked at each optimization cycle,
multiple executions must be enforced in order to achieve multi detection. This
paper presents an algorithm for automatic detection of multiple circular shapes
that considers the overall process as a multi-modal optimization problem. The
approach is based on the artificial bee colony (ABC) algorithm, a swarm
optimization algorithm inspired by the intelligent foraging behavior of honey
bees. Unlike the original ABC algorithm, the proposed approach presents the
addition of a memory for discarded solutions. Such memory allows holding
important information regarding other local optima which might have emerged
during the optimization process. The detector uses a combination of three
non-collinear edge points as parameters to determine circle candidates. A
matching function (nectar- amount) determines if such circle candidates
(bee-food-sources) are actually present in the image. Guided by the values of
such matching functions, the set of encoded candidate circles are evolved
through the ABC algorithm so that the best candidate (global optimum) can be
fitted into an actual circle within the edge only image. Then, an analysis of
the incorporated memory is executed in order to identify potential local
optima, i.e., other circles.
A Bimodal Co-Sparse Analysis Model for Image Processing
Authors: Martin Kiechle, Tim Habigt, Simon Hawe, Martin Kleinsteuber
Date: 2014-06-25 00:00:00
Tasks: Image Registration

The success of many computer vision tasks lies in the ability to exploit the
interdependency between different image modalities such as intensity and depth.
Fusing corresponding information can be achieved on several levels, and one
promising approach is the integration at a low level. Moreover, sparse signal
models have successfully been used in many vision applications. Within this
area of research, the so called co-sparse analysis model has attracted
considerably less attention than its well-known counterpart, the sparse
synthesis model, although it has been proven to be very useful in various image
processing applications. In this paper, we propose a co-sparse analysis model
that is able to capture the interdependency of two image modalities. It is
based on the assumption that a pair of analysis operators exists, so that the
co-supports of the corresponding bimodal image structures are correlated. We
propose an algorithm that is able to learn such a coupled pair of operators
from registered and noise-free training data. Furthermore, we explain how this
model can be applied to solve linear inverse problems in image processing and
how it can be used for image registration tasks. This paper extends the work of
some of the authors by two major contributions. Firstly, a modification of the
learning process is proposed that a priori guarantees unit norm and zero-mean
of the rows of the operator. This accounts for the intuition that contrast in
image modalities carries the most information. Secondly, the model is used in a
novel bimodal image registration algorithm which estimates the transformation
parameters of unregistered images of different modalities.
Weakly-supervised Discovery of Visual Pattern Configurations
Authors: Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
Date: 2014-06-25 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Object, object-detection, Object Detection

The increasing prominence of weakly labeled data nurtures a growing demand
for object detection methods that can cope with minimal supervision. We propose
an approach that automatically identifies discriminative configurations of
visual patterns that are characteristic of a given object class. We formulate
the problem as a constrained submodular optimization problem and demonstrate
the benefits of the discovered configurations in remedying mislocalizations and
finding informative positive and negative training examples. Together, these
lead to state-of-the-art weakly-supervised detection results on the challenging
PASCAL VOC dataset.
A Quantitative Neural Coding Model of Sensory Memory
Authors: Peilei Liu, Ting Wang
Date: 2014-06-25 00:00:00
Tasks: model

The coding mechanism of sensory memory on the neuron scale is one of the most
important questions in neuroscience. We have put forward a quantitative neural
network model, which is self organized, self similar, and self adaptive, just
like an ecosystem following Darwin theory. According to this model, neural
coding is a mult to one mapping from objects to neurons. And the whole cerebrum
is a real-time statistical Turing Machine, with powerful representing and
learning ability. This model can reconcile some important disputations, such
as: temporal coding versus rate based coding, grandmother cell versus
population coding, and decay theory versus interference theory. And it has also
provided explanations for some key questions such as memory consolidation,
episodic memory, consciousness, and sentiment. Philosophical significance is
indicated at last.