pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating MCMC via Parallel Predictive Prefetching
Authors: Elaine Angelino, Eddie Kohler, Amos Waterland, Margo Seltzer, Ryan P. Adams
Date: 2014-03-28 00:00:00
Tasks: Bayesian Inference

We present a general framework for accelerating a large class of widely used
Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast,
iterative approximations to the target density to speculatively evaluate many
potential future steps of the chain in parallel. The approach can accelerate
computation of the target distribution of a Bayesian inference problem, without
compromising exactness, by exploiting subsets of data. It takes advantage of
whatever parallel resources are available, but produces results exactly
equivalent to standard serial execution. In the initial burn-in phase of chain
evaluation, it achieves speedup over serial evaluation that is close to linear
in the number of available cores.
Automated polyp detection in colon capsule endoscopy
Authors: Alexander V. Mamonov, Isabel N. Figueiredo, Pedro N. Figueiredo, Yen-Hsi Richard Tsai
Date: 2013-05-08 00:00:00
Tasks: Sensitivity, Specificity

Colorectal polyps are important precursors to colon cancer, a major health
problem. Colon capsule endoscopy (CCE) is a safe and minimally invasive
examination procedure, in which the images of the intestine are obtained via
digital cameras on board of a small capsule ingested by a patient. The video
sequence is then analyzed for the presence of polyps. We propose an algorithm
that relieves the labor of a human operator analyzing the frames in the video
sequence. The algorithm acts as a binary classifier, which labels the frame as
either containing polyps or not, based on the geometrical analysis and the
texture content of the frame. The geometrical analysis is based on a
segmentation of an image with the help of a mid-pass filter. The features
extracted by the segmentation procedure are classified according to an
assumption that the polyps are characterized as protrusions that are mostly
round in shape. Thus, we use a best fit ball radius as a decision parameter of
a binary classifier. We present a statistical study of the performance of our
approach on a data set containing over 18,900 frames from the endoscopic video
sequences of five adult patients. The algorithm demonstrates a solid
performance, achieving 47% sensitivity per frame and over 81% sensitivity per
polyp at a specificity level of 90%. On average, with a video sequence length
of 3747 frames, only 367 false positive frames need to be inspected by a human
operator.
Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm: A different perspective
Authors: Feng Liu, Zhifang Wang
Date: 2014-03-27 00:00:00

In this paper we study the problem of optimal layout of an offshore wind farm
to minimize the wake effect impacts. Considering the specific requirements of
concerned offshore wind farm, we propose an adaptive genetic algorithm (AGA)
which introduces location swaps to replace random crossovers in conventional
GAs. That way the total number of turbines in the resulting layout will be
effectively kept to the initially specified value. We experiment the proposed
AGA method on three cases with free wind speed of 12 m/s, 20 m/s, and a typical
offshore wind distribution setting respectively. Numerical results verify the
effectiveness of our proposed algorithm which achieves a much faster
convergence compared to conventional GA algorithms.
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
Authors: Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari, Christoph H. Lampert
Date: 2014-03-27 00:00:00
Tasks: Form, Image Segmentation, Segmentation, Semantic Segmentation

We present LS-CRF, a new method for very efficient large-scale training of
Conditional Random Fields (CRFs). It is inspired by existing closed-form
expressions for the maximum likelihood parameters of a generative graphical
model with tree topology. LS-CRF training requires only solving a set of
independent regression problems, for which closed-form expression as well as
efficient iterative solvers are available. This makes it orders of magnitude
faster than conventional maximum likelihood learning for CRFs that require
repeated runs of probabilistic inference. At the same time, the models learned
by our method still allow for joint inference at test time. We apply LS-CRF to
the task of semantic image segmentation, showing that it is highly efficient,
even for loopy models where probabilistic inference is problematic. It allows
the training of image segmentation models from significantly larger training
sets than had been used previously. We demonstrate this on two new datasets
that form a second contribution of this paper. They consist of over 180,000
images with figure-ground segmentation annotations. Our large-scale experiments
show that the possibilities of CRF-based image segmentation are far from
exhausted, indicating, for example, that semi-supervised learning and the use
of non-linear predictors are promising directions for achieving higher
segmentation accuracy in the future.
Compressive Pattern Matching on Multispectral Data
Authors: S. Rousseau, D. Helbert, P. Carré, J. Blanc-Talon
Date: 2014-03-27 00:00:00
Tasks: Compressive Sensing

We introduce a new constrained minimization problem that performs template
and pattern detection on a multispectral image in a compressive sensing
context. We use an original minimization problem from Guo and Osher that uses
$L_1$ minimization techniques to perform template detection in a multispectral
image. We first adapt this minimization problem to work with compressive
sensing data. Then we extend it to perform pattern detection using a formal
transform called the spectralization along a pattern. That extension brings out
the problem of measurement reconstruction. We introduce shifted measurements
that allow us to reconstruct all the measurement with a small overhead and we
give an optimality constraint for simple patterns. We present numerical results
showing the performances of the original minimization problem and the
compressed ones with different measurement rates and applied on remotely sensed
data.
Pyramidal Fisher Motion for Multiview Gait Recognition
Authors: F. M. Castro, M. J. Marin-Jimenez, R. Medina-Carnicer
Date: 2014-03-27 00:00:00
Tasks: Gait Recognition, Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous
works) we propose and evaluate the use of motion descriptors based on densely
sampled short-term trajectories. We take advantage of state-of-the-art people
detectors to define custom spatial configurations of the descriptors around the
target person. Thus, obtaining a pyramidal representation of the gait motion.
The local motion features (described by the Divergence-Curl-Shear descriptor)
extracted on the different spatial areas of the person are combined into a
single high-level gait descriptor by using the Fisher Vector encoding. The
proposed approach, coined Pyramidal Fisher Motion, is experimentally validated
on the recent `AVA Multiview Gait' dataset. The results show that this new
approach achieves promising results in the problem of gait recognition.
An Optimization Method For Slice Interpolation Of Medical Images
Authors: Ahmadreza Baghaie, Zeyun Yu
Date: 2014-02-05 00:00:00
Tasks: Image Registration, Object

Slice interpolation is a fast growing field in medical image processing.
Intensity-based interpolation and object-based interpolation are two major
groups of methods in the literature. In this paper, we describe an
object-oriented, optimization method based on a modified version of
curvature-based image registration, in which a displacement field is computed
for the missing slice between two known slices and used to interpolate the
intensities of the missing slice. The proposed approach is evaluated
quantitatively by using the Mean Squared Difference (MSD) as a metric. The
produced results also show visual improvement in preserving sharp edges in
images.
Automatic Segmentation of Broadcast News Audio using Self Similarity Matrix
Authors: Sapna Soni, Ahmed Imran, Sunil Kumar Kopparapu
Date: 2014-03-27 00:00:00
Tasks: Segmentation

Generally audio news broadcast on radio is com- posed of music, commercials,
news from correspondents and recorded statements in addition to the actual news
read by the newsreader. When news transcripts are available, automatic
segmentation of audio news broadcast to time align the audio with the text
transcription to build frugal speech corpora is essential. We address the
problem of identifying segmentation in the audio news broadcast corresponding
to the news read by the newsreader so that they can be mapped to the text
transcripts. The existing techniques produce sub-optimal solutions when used to
extract newsreader read segments. In this paper, we propose a new technique
which is able to identify the acoustic change points reliably using an acoustic
Self Similarity Matrix (SSM). We describe the two pass technique in detail and
verify its performance on real audio news broadcast of All India Radio for
different languages.
Online Learning of k-CNF Boolean Functions
Authors: Joel Veness, Marcus Hutter
Date: 2014-03-26 00:00:00
Tasks: PAC learning

This paper revisits the problem of learning a k-CNF Boolean function from
examples in the context of online learning under the logarithmic loss. In doing
so, we give a Bayesian interpretation to one of Valiant's celebrated PAC
learning algorithms, which we then build upon to derive two efficient, online,
probabilistic, supervised learning algorithms for predicting the output of an
unknown k-CNF Boolean function. We analyze the loss of our methods, and show
that the cumulative log-loss can be upper bounded, ignoring logarithmic
factors, by a polynomial function of the size of each example.
KPCA Spatio-temporal trajectory point cloud classifier for recognizing human actions in a CBVR system
Authors: Iván Gómez-Conde, David N. Olivieri
Date: 2014-03-26 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization, Video Retrieval

We describe a content based video retrieval (CBVR) software system for
identifying specific locations of a human action within a full length film, and
retrieving similar video shots from a query. For this, we introduce the concept
of a trajectory point cloud for classifying unique actions, encoded in a
spatio-temporal covariant eigenspace, where each point is characterized by its
spatial location, local Frenet-Serret vector basis, time averaged curvature and
torsion and the mean osculating hyperplane. Since each action can be
distinguished by their unique trajectories within this space, the trajectory
point cloud is used to define an adaptive distance metric for classifying
queries against stored actions. Depending upon the distance to other
trajectories, the distance metric uses either large scale structure of the
trajectory point cloud, such as the mean distance between cloud centroids or
the difference in hyperplane orientation, or small structure such as the time
averaged curvature and torsion, to classify individual points in a fuzzy-KNN.
Our system can function in real-time and has an accuracy greater than 93% for
multiple action recognition within video repositories. We demonstrate the use
of our CBVR system in two situations: by locating specific frame positions of
trained actions in two full featured films, and video shot retrieval from a
database with a web search application.
Optimized imaging using non-rigid registration
Authors: Benjamin Berkels, Peter Binev, Douglas A. Blom, Wolfgang Dahmen, Robert C. Sharpley, Thomas Vogt
Date: 2014-03-26 00:00:00

The extraordinary improvements of modern imaging devices offer access to data
with unprecedented information content. However, widely used image processing
methodologies fall far short of exploiting the full breadth of information
offered by numerous types of scanning probe, optical, and electron
microscopies. In many applications, it is necessary to keep measurement
intensities below a desired threshold. We propose a methodology for extracting
an increased level of information by processing a series of data sets
suffering, in particular, from high degree of spatial uncertainty caused by
complex multiscale motion during the acquisition process. An important role is
played by a nonrigid pixel-wise registration method that can cope with low
signal-to-noise ratios. This is accompanied by formulating objective quality
measures which replace human intervention and visual inspection in the
processing chain. Scanning transmission electron microscopy of siliceous
zeolite material exhibits the above-mentioned obstructions and therefore serves
as orientation and a test of our procedures.
Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example
Authors: Xiaomin Lin, Peter A. Beling, Randy Cogill
Date: 2014-03-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We compare the performance of Inverse Reinforcement Learning (IRL) with the
relative new model of Multi-agent Inverse Reinforcement Learning (MIRL). Before
comparing the methods, we extend a published Bayesian IRL approach that is only
applicable to the case where the reward is only state dependent to a general
one capable of tackling the case where the reward depends on both state and
action. Comparison between IRL and MIRL is made in the context of an abstract
soccer game, using both a game model in which the reward depends only on state
and one in which it depends on both state and action. Results suggest that the
IRL approach performs much worse than the MIRL approach. We speculate that the
underperformance of IRL is because it fails to capture equilibrium information
in the manner possible in MIRL.
Beyond L2-Loss Functions for Learning Sparse Models
Authors: Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan
Date: 2014-03-26 00:00:00
Tasks: Clustering, Retrieval, Sparse Learning, TAG

Incorporating sparsity priors in learning tasks can give rise to simple, and
interpretable models for complex high dimensional data. Sparse models have
found widespread use in structure discovery, recovering data from corruptions,
and a variety of large scale unsupervised and supervised learning problems.
Assuming the availability of sufficient data, these methods infer dictionaries
for sparse representations by optimizing for high-fidelity reconstruction. In
most scenarios, the reconstruction quality is measured using the squared
Euclidean distance, and efficient algorithms have been developed for both batch
and online learning cases. However, new application domains motivate looking
beyond conventional loss functions. For example, robust loss functions such as
$\ell_1$ and Huber are useful in learning outlier-resilient models, and the
quantile loss is beneficial in discovering structures that are the
representative of a particular quantile. These new applications motivate our
work in generalizing sparse learning to a broad class of convex loss functions.
In particular, we consider the class of piecewise linear quadratic (PLQ) cost
functions that includes Huber, as well as $\ell_1$, quantile, Vapnik, hinge
loss, and smoothed variants of these penalties. We propose an algorithm to
learn dictionaries and obtain sparse codes when the data reconstruction
fidelity is measured using any smooth PLQ cost function. We provide convergence
guarantees for the proposed algorithm, and demonstrate the convergence behavior
using empirical experiments. Furthermore, we present three case studies that
require the use of PLQ cost functions: (i) robust image modeling, (ii) tag
refinement for image annotation and retrieval and (iii) computing empirical
confidence limits for subspace clustering.
Sign Language Lexical Recognition With Propositional Dynamic Logic
Authors: Arturo Curiel, Christophe Collet
Date: 2014-03-26 00:00:00
Proceeding: ACL 2013 8

This paper explores the use of Propositional Dynamic Logic (PDL) as a
suitable formal framework for describing Sign Language (SL), the language of
deaf people, in the context of natural language processing. SLs are visual,
complete, standalone languages which are just as expressive as oral languages.
Signs in SL usually correspond to sequences of highly specific body postures
interleaved with movements, which make reference to real world objects,
characters or situations. Here we propose a formal representation of SL signs,
that will help us with the analysis of automatically-collected hand tracking
data from French Sign Language (FSL) video corpora. We further show how such a
representation could help us with the design of computer aided SL verification
tools, which in turn would bring us closer to the development of an automatic
recognition system for these languages.
QCMC: Quasi-conformal Parameterizations for Multiply-connected domains
Authors: Kin Tat Ho, Lok Ming Lui
Date: 2014-03-26 00:00:00

This paper presents a method to compute the {\it quasi-conformal
parameterization} (QCMC) for a multiply-connected 2D domain or surface. QCMC
computes a quasi-conformal map from a multiply-connected domain $S$ onto a
punctured disk $D_S$ associated with a given Beltrami differential. The
Beltrami differential, which measures the conformality distortion, is a
complex-valued function $\mu:S\to\mathbb{C}$ with supremum norm strictly less
than 1. Every Beltrami differential gives a conformal structure of $S$. Hence,
the conformal module of $D_S$, which are the radii and centers of the inner
circles, can be fully determined by $\mu$, up to a M\"obius transformation. In
this paper, we propose an iterative algorithm to simultaneously search for the
conformal module and the optimal quasi-conformal parameterization. The key idea
is to minimize the Beltrami energy subject to the boundary constraints. The
optimal solution is our desired quasi-conformal parameterization onto a
punctured disk. The parameterization of the multiply-connected domain
simplifies numerical computations and has important applications in various
fields, such as in computer graphics and vision. Experiments have been carried
out on synthetic data together with real multiply-connected Riemann surfaces.
Results show that our proposed method can efficiently compute quasi-conformal
parameterizations of multiply-connected domains and outperforms other
state-of-the-art algorithms. Applications of the proposed parameterization
technique have also been explored.
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?
Authors: Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso Poggio
Date: 2013-11-16 00:00:00
Tasks: Face Detection, Face Recognition

The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be dissatisfied with it, among them is its lack of biological plausibility. A recent theory of invariant recognition by feedforward hierarchical networks, like HMAX, other convolutional networks, or possibly the ventral stream, implies an alternative approach to unconstrained face recognition. This approach accomplishes detection and alignment implicitly by storing transformations of training images (called templates) rather than explicitly detecting and aligning faces at test time. Here we propose a particular locality-sensitive hashing based voting scheme which we call "consensus of collisions" and show that it can be used to approximate the full 3-layer hierarchy implied by the theory. The resulting end-to-end system for unconstrained face recognition operates on photographs of faces taken under natural conditions, e.g., Labeled Faces in the Wild (LFW), without aligning or cropping them, as is normally done. It achieves a drastic improvement in the state of the art on this end-to-end task, reaching the same level of performance as the best systems operating on aligned, closely cropped images (no outside training data). It also performs well on two newer datasets, similar to LFW, but more difficult: LFW-jittered (new here) and SUFR-W.
A study on cost behaviors of binary classification measures in class-imbalanced problems
Authors: Bao-Gang Hu, Wei-Ming Dong
Date: 2014-03-26 00:00:00
Tasks: Binary Classification, General Classification

This work investigates into cost behaviors of binary classification measures
in a background of class-imbalanced problems. Twelve performance measures are
studied, such as F measure, G-means in terms of accuracy rates, and of recall
and precision, balance error rate (BER), Matthews correlation coefficient
(MCC), Kappa coefficient, etc. A new perspective is presented for those
measures by revealing their cost functions with respect to the class imbalance
ratio. Basically, they are described by four types of cost functions. The
functions provides a theoretical understanding why some measures are suitable
for dealing with class-imbalanced problems. Based on their cost functions, we
are able to conclude that G-means of accuracy rates and BER are suitable
measures because they show "proper" cost behaviors in terms of "a
misclassification from a small class will cause a greater cost than that from a
large class". On the contrary, F1 measure, G-means of recall and precision, MCC
and Kappa coefficient measures do not produce such behaviors so that they are
unsuitable to serve our goal in dealing with the problems properly.
Evaluating topic coherence measures
Authors: Frank Rosner, Alexander Hinneburg, Michael Röder, Martin Nettling, Andreas Both
Date: 2014-03-25 00:00:00
Tasks: Philosophy, Topic Models

Topic models extract representative word sets - called topics - from word
counts in documents without requiring any semantic annotations. Topics are not
guaranteed to be well interpretable, therefore, coherence measures have been
proposed to distinguish between good and bad topics. Studies of topic coherence
so far are limited to measures that score pairs of individual words. For the
first time, we include coherence measures from scientific philosophy that score
pairs of more complex word subsets and apply them to topic scoring.
Stabilizing dual-energy X-ray computed tomography reconstructions using patch-based regularization
Authors: Brian H. Tracey, Eric L. Miller
Date: 2014-03-25 00:00:00
Tasks: Computed Tomography (CT)

Recent years have seen growing interest in exploiting dual- and multi-energy
measurements in computed tomography (CT) in order to characterize material
properties as well as object shape. Material characterization is performed by
decomposing the scene into constitutive basis functions, such as Compton
scatter and photoelectric absorption functions. While well motivated
physically, the joint recovery of the spatial distribution of photoelectric and
Compton properties is severely complicated by the fact that the data are
several orders of magnitude more sensitive to Compton scatter coefficients than
to photoelectric absorption, so small errors in Compton estimates can create
large artifacts in the photoelectric estimate. To address these issues, we
propose a model-based iterative approach which uses patch-based regularization
terms to stabilize inversion of photoelectric coefficients, and solve the
resulting problem though use of computationally attractive Alternating
Direction Method of Multipliers (ADMM) solution techniques. Using simulations
and experimental data acquired on a commercial scanner, we demonstrate that the
proposed processing can lead to more stable material property estimates which
should aid materials characterization in future dual- and multi-energy CT
systems.