pwc-archive/papers-with-abstracts huggingface dataset:
Image Inpainting Using Directional Tensor Product Complex Tight Framelets
Authors: Yi Shen, Bin Han, Elena Braverman
Date: 2014-07-11 00:00:00
Tasks: Denoising, Image Denoising, Image Inpainting, Image Restoration

In this paper we are particularly interested in the image inpainting problem
using directional complex tight wavelet frames. Under the assumption that frame
coefficients of images are sparse, several iterative thresholding algorithms
for the image inpainting problem have been proposed in the literature. The
outputs of such iterative algorithms are closely linked to solutions of several
convex minimization models using the balanced approach which simultaneously
combines the $l_1$-regularization for sparsity of frame coefficients and the
$l_2$-regularization for smoothness of the solution. Due to the redundancy of a
tight frame, elements of a tight frame could be highly correlated and
therefore, their corresponding frame coefficients of an image are expected to
close to each other. This is called the grouping effect in statistics. In this
paper, we establish the grouping effect property for frame-based convex
minimization models using the balanced approach. This result on grouping effect
partially explains the effectiveness of models using the balanced approach for
several image restoration problems. Inspired by recent development on
directional tensor product complex tight framelets (TP-CTFs) and their
impressive performance for the image denoising problem, in this paper we
propose an iterative thresholding algorithm using a single tight frame derived
from TP-CTFs for the image inpainting problem. Experimental results show that
our proposed algorithm can handle well both cartoons and textures
simultaneously and performs comparably and often better than several well-known
frame-based iterative thresholding algorithms for the image inpainting problem
without noise. For the image inpainting problem with additive zero-mean i.i.d.
Gaussian noise, our proposed algorithm using TP-CTFs performs superior than
other known state-of-the-art frame-based image inpainting algorithms.
Algorithmic Identification of Probabilities
Authors: Paul M. B. Vitanyi, Nick Chater
Date: 2013-11-28 00:00:00

TThe problem is to identify a probability associated with a set of natural
numbers, given an infinite data sequence of elements from the set. If the given
sequence is drawn i.i.d. and the probability mass function involved (the
target) belongs to a computably enumerable (c.e.) or co-computably enumerable
(co-c.e.) set of computable probability mass functions, then there is an
algorithm to almost surely identify the target in the limit. The technical tool
is the strong law of large numbers. If the set is finite and the elements of
the sequence are dependent while the sequence is typical in the sense of
Martin-L\"of for at least one measure belonging to a c.e. or co-c.e. set of
computable measures, then there is an algorithm to identify in the limit a
computable measure for which the sequence is typical (there may be more than
one such measure). The technical tool is the theory of Kolmogorov complexity.
We give the algorithms and consider the associated predictions.
Decision-Making with Complex Data Structures using Probabilistic Programming
Authors: Brian E. Ruttenberg, Avi Pfeffer
Date: 2014-07-11 00:00:00
Tasks: Decision Making, Probabilistic Programming

Existing decision-theoretic reasoning frameworks such as decision networks
use simple data structures and processes. However, decisions are often made
based on complex data structures, such as social networks and protein
sequences, and rich processes involving those structures. We present a
framework for representing decision problems with complex data structures using
probabilistic programming, allowing probabilistic models to be created with
programming language constructs such as data structures and control flow. We
provide a way to use arbitrary data types with minimal effort from the user,
and an approximate decision-making algorithm that is effective even when the
information space is very large or infinite. Experimental results show our
algorithm working on problems with very large information spaces.
Universal Matrix Completion
Authors: Srinadh Bhojanapalli, Prateek Jain
Date: 2014-02-10 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

The problem of low-rank matrix completion has recently generated a lot of
interest leading to several results that offer exact solutions to the problem.
However, in order to do so, these methods make assumptions that can be quite
restrictive in practice. More specifically, the methods assume that: a) the
observed indices are sampled uniformly at random, and b) for every new matrix,
the observed indices are sampled afresh. In this work, we address these issues
by providing a universal recovery guarantee for matrix completion that works
for a variety of sampling schemes. In particular, we show that if the set of
sampled indices come from the edges of a bipartite graph with large spectral
gap (i.e. gap between the first and the second singular value), then the
nuclear norm minimization based method exactly recovers all low-rank matrices
that satisfy certain incoherence properties. Moreover, we also show that under
certain stricter incoherence conditions, $O(nr^2)$ uniformly sampled entries
are enough to recover any rank-$r$ $n\times n$ matrix, in contrast to the
$O(nr\log n)$ sample complexity required by other matrix completion algorithms
as well as existing analyses of the nuclear norm method.
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Authors: Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Date: 2014-07-11 00:00:00
Tasks: Denoising, Image Denoising

Low-resolution and signal-dependent noise distribution in positron emission
tomography (PET) images makes denoising process an inevitable step prior to
qualitative and quantitative image analysis tasks. Conventional PET denoising
methods either over-smooth small-sized structures due to resolution limitation
or make incorrect assumptions about the noise characteristics. Therefore,
clinically important quantitative information may be corrupted. To address
these challenges, we introduced a novel approach to remove signal-dependent
noise in the PET images where the noise distribution was considered as
Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation
(GAT) was used to stabilize varying nature of the PET noise. Other than noise
stabilization, it is also desirable for the noise removal filter to preserve
the boundaries of the structures while smoothing the noisy regions. Indeed, it
is important to avoid significant loss of quantitative information such as
standard uptake value (SUV)-based metrics as well as metabolic lesion volume.
To satisfy all these properties, we extended bilateral filtering method into
trilateral filtering through multiscaling and optimal Gaussianization process.
The proposed method was tested on more than 50 PET-CT images from various
patients having different cancers and achieved the superior performance
compared to the widely used denoising techniques in the literature.
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
Authors: Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Date: 2014-07-11 00:00:00
Tasks: Computed Tomography (CT), General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Accurate delineation of pathological lungs from computed tomography (CT)
images remains mostly unsolved because available methods fail to provide a
reliable generic solution due to high variability of abnormality appearance.
Local descriptor-based classification methods have shown to work well in
annotating pathologies; however, these methods are usually computationally
intensive which restricts their widespread use in real-time or near-real-time
clinical applications. In this paper, we present a novel approach for fast,
accurate, reliable segmentation of pathological lungs from CT scans by
combining region-based segmentation method with local descriptor classification
that is performed on an optimized sampling grid. Our method works in two
stages; during stage one, we adapted the fuzzy connectedness (FC) image
segmentation algorithm to perform initial lung parenchyma extraction. In the
second stage, texture-based local descriptors are utilized to segment abnormal
imaging patterns using a near optimal keypoint analysis by employing centroid
of supervoxel as grid points. The quantitative results show that our
pathological lung segmentation method is fast, robust, and improves on current
standards and has potential to enhance the performance of routine clinical
tasks.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Authors: Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Deborah Douglas, Jeffrey M. Solomon, Jayaram K. Udupa, Daniel J. Mollura
Date: 2014-07-11 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Segmentation, Semantic Segmentation

Accurate and fast extraction of lung volumes from computed tomography (CT)
scans remains in a great demand in the clinical environment because the
available methods fail to provide a generic solution due to wide anatomical
variations of lungs and existence of pathologies. Manual annotation, current
gold standard, is time consuming and often subject to human bias. On the other
hand, current state-of-the-art fully automated lung segmentation methods fail
to make their way into the clinical practice due to their inability to
efficiently incorporate human input for handling misclassifications and praxis.
This paper presents a lung annotation tool for CT images that is interactive,
efficient, and robust. The proposed annotation tool produces an "as accurate as
possible" initial annotation based on the fuzzy-connectedness image
segmentation, followed by efficient manual fixation of the initial extraction
if deemed necessary by the practitioner. To provide maximum flexibility to the
users, our annotation tool is supported in three major operating systems
(Windows, Linux, and the Mac OS X). The quantitative results comparing our free
software with commercially available lung segmentation tools show higher degree
of consistency and precision of our software with a considerable potential to
enhance the performance of routine clinical tasks.
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
Authors: Guanjiao Ren, Weihai Chen, Sakyasingha Dasgupta, Christoph Kolodziejski, Florentin Wörgötter, Poramate Manoonpong
Date: 2014-07-11 00:00:00

An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.
Charge Scheduling of an Energy Storage System under Time-of-use Pricing and a Demand Charge
Authors: Yourim Yoon, Yong-Hyuk Kim
Date: 2014-07-11 00:00:00
Tasks: Scheduling

A real-coded genetic algorithm is used to schedule the charging of an energy
storage system (ESS), operated in tandem with renewable power by an electricity
consumer who is subject to time-of-use pricing and a demand charge. Simulations
based on load and generation profiles of typical residential customers show
that an ESS scheduled by our algorithm can reduce electricity costs by
approximately 17%, compared to a system without an ESS, and by 8% compared to a
scheduling algorithm based on net power.
Agent Behavior Prediction and Its Generalization Analysis
Authors: Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu
Date: 2014-04-19 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Machine learning algorithms have been applied to predict agent behaviors in
real-world dynamic systems, such as advertiser behaviors in sponsored search
and worker behaviors in crowdsourcing. The behavior data in these systems are
generated by live agents: once the systems change due to the adoption of the
prediction models learnt from the behavior data, agents will observe and
respond to these changes by changing their own behaviors accordingly. As a
result, the behavior data will evolve and will not be identically and
independently distributed, posing great challenges to the theoretical analysis
on the machine learning algorithms for behavior prediction. To tackle this
challenge, in this paper, we propose to use Markov Chain in Random Environments
(MCRE) to describe the behavior data, and perform generalization analysis of
the machine learning algorithms on its basis. Since the one-step transition
probability matrix of MCRE depends on both previous states and the random
environment, conventional techniques for generalization analysis cannot be
directly applied. To address this issue, we propose a novel technique that
transforms the original MCRE into a higher-dimensional time-homogeneous Markov
chain. The new Markov chain involves more variables but is more regular, and
thus easier to deal with. We prove the convergence of the new Markov chain when
time approaches infinity. Then we prove a generalization bound for the machine
learning algorithms on the behavior data generated by the new Markov chain,
which depends on both the Markovian parameters and the covering number of the
function class compounded by the loss function for behavior prediction and the
behavior prediction model. To the best of our knowledge, this is the first work
that performs the generalization analysis on data generated by complex
processes in real-world dynamic systems.
An SVM Based Approach for Cardiac View Planning
Authors: Ramasubramanian Sundararajan, Hima Patel, Dattesh Shanbhag, Vivek Vaidya
Date: 2014-07-11 00:00:00
Tasks: feature selection

We consider the problem of automatically prescribing oblique planes (short
axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging
(MRI). A concern with technologist-driven acquisitions of these planes is the
quality and time taken for the total examination. We propose an automated
solution incorporating anatomical features external to the cardiac region. The
solution uses support vector machine regression models wherein complexity and
feature selection are optimized using multi-objective genetic algorithms.
Additionally, we examine the robustness of our approach by training our models
on images with additive Rician-Gaussian mixtures at varying Signal to Noise
(SNR) levels. Our approach has shown promising results, with an angular
deviation of less than 15 degrees on 90% cases across oblique planes, measured
in terms of average 6-fold cross validation performance -- this is generally
within acceptable bounds of variation as specified by clinicians.
Biclustering Via Sparse Clustering
Authors: Qian Liu, Guanhua Chen, Michael R. Kosorok, Eric Bair
Date: 2014-07-11 00:00:00
Tasks: Clustering

In many situations it is desirable to identify clusters that differ with
respect to only a subset of features. Such clusters may represent homogeneous
subgroups of patients with a disease, such as cancer or chronic pain. We define
a bicluster to be a submatrix U of a larger data matrix X such that the
features and observations in U differ from those not contained in U. For
example, the observations in U could have different means or variances with
respect to the features in U. We propose a general framework for biclustering
based on the sparse clustering method of Witten and Tibshirani (2010). We
develop a method for identifying features that belong to biclusters. This
framework can be used to identify biclusters that differ with respect to the
means of the features, the variance of the features, or more general
differences. We apply these methods to several simulated and real-world data
sets and compare the results of our method with several previously published
methods. The results of our method compare favorably with existing methods with
respect to both predictive accuracy and computing time.
Evolutionary Robotics on the Web with WebGL and Javascript
Authors: Jared Moore, Anthony Clark, Philip McKinley
Date: 2014-06-12 00:00:00

Web-based applications are highly accessible to users, providing rich,
interactive content while eliminating the need to install software locally.
However, evolutionary robotics (ER) has faced challenges in this domain as
web-based technologies have not been amenable to 3D physics simulations.
Traditionally, physics-based simulations require a local installation and a
high degree of user knowledge to configure an environment, but the emergence of
Javascript-based physics engines enables complex simulations to be executed in
web browsers. These developments create opportunities for ER research to reach
new audiences by increasing accessibility. In this work, we introduce two
web-based tools we have built to facilitate the exchange of ideas with other
researchers as well as outreach to K-12 students and the general public. The
first tool is intended to distribute and exchange ER research results, while
the second is a completely browser-based implementation of an ER environment.
A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain
Authors: Paul Szerlip, Kenneth O. Stanley
Date: 2014-07-11 00:00:00

To address the difficulty of creating online collaborative evolutionary
systems, this paper presents a new prototype library called Worldwide
Infrastructure for Neuroevolution (WIN) and its accompanying site WIN Online
(http://winark.org/). The WIN library is a collection of software packages
built on top of Node.js that reduce the complexity of creating fully
persistent, online, and interactive (or automated) evolutionary platforms
around any domain. WIN Online is the public interface for WIN, providing an
online collection of domains built with the WIN library that lets novice and
expert users browse and meaningfully contribute to ongoing experiments. The
long term goal of WIN is to make it trivial to connect any platform to the
world, providing both a stream of online users, and archives of data and
discoveries for later extension by humans or computers.
Hidden Markov Model Based Part of Speech Tagger for Sinhala Language
Authors: A. J. P. M. P. Jayaweera, N. G. J. Dias
Date: 2014-07-10 00:00:00
Tasks: POS, TAG

In this paper we present a fundamental lexical semantics of Sinhala language
and a Hidden Markov Model (HMM) based Part of Speech (POS) Tagger for Sinhala
language. In any Natural Language processing task, Part of Speech is a very
vital topic, which involves analysing of the construction, behaviour and the
dynamics of the language, which the knowledge could utilized in computational
linguistics analysis and automation applications. Though Sinhala is a
morphologically rich and agglutinative language, in which words are inflected
with various grammatical features, tagging is very essential for further
analysis of the language. Our research is based on statistical based approach,
in which the tagging process is done by computing the tag sequence probability
and the word-likelihood probability from the given corpus, where the linguistic
knowledge is automatically extracted from the annotated corpus. The current
tagger could reach more than 90% of accuracy for known words.
FAME: Face Association through Model Evolution
Authors: Eren Golge, Pinar Duygulu
Date: 2014-07-10 00:00:00
Tasks: Face Detection, Face Identification, model

We attack the problem of learning face models for public faces from weakly-labelled images collected from web through querying a name. The data is very noisy even after face detection, with several irrelevant faces corresponding to other people. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the face models associated to a name to evolve. The idea is based on capturing discriminativeness and representativeness of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with possibly different characteristics. On benchmark datasets, our results are comparable to or better than state-of-the-art studies for the task of face identification.
Algorithms for CVaR Optimization in MDPs
Authors: Yin-Lam Chow, Mohammad Ghavamzadeh
Date: 2014-06-12 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Decision Making, Sequential Decision Making

In many sequential decision-making problems we may want to manage risk by
minimizing some measure of variability in costs in addition to minimizing a
standard criterion. Conditional value-at-risk (CVaR) is a relatively new risk
measure that addresses some of the shortcomings of the well-known
variance-related risk measures, and because of its computational efficiencies
has gained popularity in finance and operations research. In this paper, we
consider the mean-CVaR optimization problem in MDPs. We first derive a formula
for computing the gradient of this risk-sensitive objective function. We then
devise policy gradient and actor-critic algorithms that each uses a specific
method to estimate this gradient and updates the policy parameters in the
descent direction. We establish the convergence of our algorithms to locally
risk-sensitive optimal policies. Finally, we demonstrate the usefulness of our
algorithms in an optimal stopping problem.
Efficiently inferring community structure in bipartite networks
Authors: Daniel B. Larremore, Aaron Clauset, Abigail Z. Jacobs
Date: 2014-03-12 00:00:00
Tasks: Community Detection, Stochastic Block Model

Bipartite networks are a common type of network data in which there are two
types of vertices, and only vertices of different types can be connected. While
bipartite networks exhibit community structure like their unipartite
counterparts, existing approaches to bipartite community detection have
drawbacks, including implicit parameter choices, loss of information through
one-mode projections, and lack of interpretability. Here we solve the community
detection problem for bipartite networks by formulating a bipartite stochastic
block model, which explicitly includes vertex type information and may be
trivially extended to $k$-partite networks. This bipartite stochastic block
model yields a projection-free and statistically principled method for
community detection that makes clear assumptions and parameter choices and
yields interpretable results. We demonstrate this model's ability to
efficiently and accurately find community structure in synthetic bipartite
networks with known structure and in real-world bipartite networks with unknown
structure, and we characterize its performance in practical contexts.