pwc-archive/papers-with-abstracts huggingface dataset:
Motion Deblurring for Plenoptic Images
Authors: Paramanand Chandramouli, Paolo Favaro, Daniele Perrone
Date: 2014-08-16 00:00:00
Tasks: Deblurring, distortion correction

We address for the first time the issue of motion blur in light field images
captured from plenoptic cameras. We propose a solution to the estimation of a
sharp high resolution scene radiance given a blurry light field image, when the
motion blur point spread function is unknown, i.e., the so-called blind
deconvolution problem. In a plenoptic camera, the spatial sampling in each view
is not only decimated but also defocused. Consequently, current blind
deconvolution approaches for traditional cameras are not applicable. Due to the
complexity of the imaging model, we investigate first the case of uniform
(shift-invariant) blur of Lambertian objects, i.e., when objects are
sufficiently far away from the camera to be approximately invariant to depth
changes and their reflectance does not vary with the viewing direction. We
introduce a highly parallelizable model for light field motion blur that is
computationally and memory efficient. We then adapt a regularized blind
deconvolution approach to our model and demonstrate its performance on both
synthetic and real light field data. Our method handles practical issues in
real cameras such as radial distortion correction and alignment within an
energy minimization framework.
Identifying Diabetic Patients with High Risk of Readmission
Authors: Malladihalli S Bhuvan, Ankit Kumar, Adil Zafar, Vinith Kishore
Date: 2016-02-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Hospital readmissions are expensive and reflect the inadequacies in
healthcare system. In the United States alone, treatment of readmitted diabetic
patients exceeds 250 million dollars per year. Early identification of patients
facing a high risk of readmission can enable healthcare providers to to conduct
additional investigations and possibly prevent future readmissions. This not
only improves the quality of care but also reduces the medical expenses on
readmission. Machine learning methods have been leveraged on public health data
to build a system for identifying diabetic patients facing a high risk of
future readmission. Number of inpatient visits, discharge disposition and
admission type were identified as strong predictors of readmission. Further, it
was found that the number of laboratory tests and discharge disposition
together predict whether the patient will be readmitted shortly after being
discharged from the hospital (i.e. <30 days) or after a longer period of time
(i.e. >30 days). These insights can help healthcare providers to improve
inpatient diabetic care. Finally, the cost analysis suggests that \$252.76
million can be saved across 98,053 diabetic patient encounters by incorporating
the proposed cost sensitive analysis model.
Scale-free network optimization: foundations and algorithms
Authors: Patrick Rebeschini, Sekhar Tatikonda
Date: 2016-02-12 00:00:00
Tasks: Sensitivity

We investigate the fundamental principles that drive the development of
scalable algorithms for network optimization. Despite the significant amount of
work on parallel and decentralized algorithms in the optimization community,
the methods that have been proposed typically rely on strict separability
assumptions for objective function and constraints. Beside sparsity, these
methods typically do not exploit the strength of the interaction between
variables in the system. We propose a notion of correlation in constrained
optimization that is based on the sensitivity of the optimal solution upon
perturbations of the constraints. We develop a general theory of sensitivity of
optimizers the extends beyond the infinitesimal setting. We present instances
in network optimization where the correlation decays exponentially fast with
respect to the natural distance in the network, and we design algorithms that
can exploit this decay to yield dimension-free optimization. Our results are
the first of their kind, and open new possibilities in the theory of local
algorithms.
A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
Authors: Fang Li, Stanley Osher, Jing Qin, Ming Yan
Date: 2015-04-09 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper, we propose a variational multiphase image segmentation model
based on fuzzy membership functions and L1-norm fidelity. Then we apply the
alternating direction method of multipliers to solve an equivalent problem. All
the subproblems can be solved efficiently. Specifically, we propose a fast
method to calculate the fuzzy median. Experimental results and comparisons show
that the L1-norm based method is more robust to outliers such as impulse noise
and keeps better contrast than its L2-norm counterpart. Theoretically, we prove
the existence of the minimizer and analyze the convergence of the algorithm.
Pursuits in Structured Non-Convex Matrix Factorizations
Authors: Rajiv Khanna, Michael Tschannen, Martin Jaggi
Date: 2016-02-12 00:00:00

Efficiently representing real world data in a succinct and parsimonious
manner is of central importance in many fields. We present a generalized greedy
pursuit framework, allowing us to efficiently solve structured matrix
factorization problems, where the factors are allowed to be from arbitrary sets
of structured vectors. Such structure may include sparsity, non-negativeness,
order, or a combination thereof. The algorithm approximates a given matrix by a
linear combination of few rank-1 matrices, each factorized into an outer
product of two vector atoms of the desired structure. For the non-convex
subproblems of obtaining good rank-1 structured matrix atoms, we employ and
analyze a general atomic power method. In addition to the above applications,
we prove linear convergence for generalized pursuit variants in Hilbert spaces
- for the task of approximation over the linear span of arbitrary dictionaries
- which generalizes OMP and is useful beyond matrix problems. Our experiments
on real datasets confirm both the efficiency and also the broad applicability
of our framework in practice.
Identifying Structures in Social Conversations in NSCLC Patients through the Semi-Automatic extraction of Topical Taxonomies
Authors: Giancarlo Crocetti, Amir A. Delay, Fatemeh Seyedmendhi
Date: 2016-02-12 00:00:00

The exploration of social conversations for addressing patient's needs is an
important analytical task in which many scholarly publications are contributing
to fill the knowledge gap in this area. The main difficulty remains the
inability to turn such contributions into pragmatic processes the
pharmaceutical industry can leverage in order to generate insight from social
media data, which can be considered as one of the most challenging source of
information available today due to its sheer volume and noise. This study is
based on the work by Scott Spangler and Jeffrey Kreulen and applies it to
identify structure in social media through the extraction of a topical taxonomy
able to capture the latent knowledge in social conversations in health-related
sites. The mechanism for automatically identifying and generating a taxonomy
from social conversations is developed and pressured tested using public data
from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category's label and the
determination of an optimal number of categories is presented which extends
Scott and Jeffrey's research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
Generalized Conjugate Gradient Methods for $\ell_1$ Regularized Convex Quadratic Programming with Finite Convergence
Authors: Zhaosong Lu, Xiaojun Chen
Date: 2015-11-24 00:00:00

The conjugate gradient (CG) method is an efficient iterative method for
solving large-scale strongly convex quadratic programming (QP). In this paper
we propose some generalized CG (GCG) methods for solving the
$\ell_1$-regularized (possibly not strongly) convex QP that terminate at an
optimal solution in a finite number of iterations. At each iteration, our
methods first identify a face of an orthant and then either perform an exact
line search along the direction of the negative projected minimum-norm
subgradient of the objective function or execute a CG subroutine that conducts
a sequence of CG iterations until a CG iterate crosses the boundary of this
face or an approximate minimizer of over this face or a subface is found. We
determine which type of step should be taken by comparing the magnitude of some
components of the minimum-norm subgradient of the objective function to that of
its rest components. Our analysis on finite convergence of these methods makes
use of an error bound result and some key properties of the aforementioned
exact line search and the CG subroutine. We also show that the proposed methods
are capable of finding an approximate solution of the problem by allowing some
inexactness on the execution of the CG subroutine. The overall arithmetic
operation cost of our GCG methods for finding an $\epsilon$-optimal solution
depends on $\epsilon$ in $O(\log(1/\epsilon))$, which is superior to the
accelerated proximal gradient method [2,23] that depends on $\epsilon$ in
$O(1/\sqrt{\epsilon})$. In addition, our GCG methods can be extended
straightforwardly to solve box-constrained convex QP with finite convergence.
Numerical results demonstrate that our methods are very favorable for solving
ill-conditioned problems.
Recovering metric from full ordinal information
Authors: Thibaut Le Gouic
Date: 2015-06-11 00:00:00

Given a geodesic space (E, d), we show that full ordinal knowledge on the
metric d-i.e. knowledge of the function D d : (w, x, y, z) $\rightarrow$ 1
d(w,x)$\le$d(y,z) , determines uniquely-up to a constant factor-the metric d.
For a subspace En of n points of E, converging in Hausdorff distance to E, we
construct a metric dn on En, based only on the knowledge of D d on En and
establish a sharp upper bound of the Gromov-Hausdorff distance between (En, dn)
and (E, d).
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Authors: Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Date: 2016-02-12 00:00:00
Tasks: Gaussian Processes, regression

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations
of Gaussian processes (GPs) and are formally equivalent to neural networks with
multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic
models and as such are arguably more flexible, have a greater capacity to
generalise, and provide better calibrated uncertainty estimates than
alternative deep models. This paper develops a new approximate Bayesian
learning scheme that enables DGPs to be applied to a range of medium to large
scale regression problems for the first time. The new method uses an
approximate Expectation Propagation procedure and a novel and efficient
extension of the probabilistic backpropagation algorithm for learning. We
evaluate the new method for non-linear regression on eleven real-world
datasets, showing that it always outperforms GP regression and is almost always
better than state-of-the-art deterministic and sampling-based approximate
inference methods for Bayesian neural networks. As a by-product, this work
provides a comprehensive analysis of six approximate Bayesian methods for
training neural networks.
Fast and Robust Hand Tracking Using Detection-Guided Optimization
Authors: Srinath Sridhar, Franziska Mueller, Antti Oulasvirta, Christian Theobalt
Date: 2016-02-12 00:00:00
Proceeding: CVPR 2015 6
Tasks: GPU, Pose Estimation

Markerless tracking of hands and fingers is a promising enabler for
human-computer interaction. However, adoption has been limited because of
tracking inaccuracies, incomplete coverage of motions, low framerate, complex
camera setups, and high computational requirements. In this paper, we present a
fast method for accurately tracking rapid and complex articulations of the hand
using a single depth camera. Our algorithm uses a novel detection-guided
optimization strategy that increases the robustness and speed of pose
estimation. In the detection step, a randomized decision forest classifies
pixels into parts of the hand. In the optimization step, a novel objective
function combines the detected part labels and a Gaussian mixture
representation of the depth to estimate a pose that best fits the depth. Our
approach needs comparably less computational resources which makes it extremely
fast (50 fps without GPU support). The approach also supports varying static,
or moving, camera-to-scene arrangements. We show the benefits of our method by
evaluating on public datasets and comparing against previous work.
An Empirical Study on Academic Commentary and Its Implications on Reading and Writing
Authors: Tai Wang, Xiangen Hu, Keith Shubeck, Zhiqiang Cai, Jie Tang
Date: 2016-02-12 00:00:00

The relationship between reading and writing (RRW) is one of the major themes
in learning science. One of its obstacles is that it is difficult to define or
measure the latent background knowledge of the individual. However, in an
academic research setting, scholars are required to explicitly list their
background knowledge in the citation sections of their manuscripts. This unique
opportunity was taken advantage of to observe RRW, especially in the published
academic commentary scenario. RRW was visualized under a proposed topic process
model by using a state of the art version of latent Dirichlet allocation (LDA).
The empirical study showed that the academic commentary is modulated both by
its target paper and the author's background knowledge. Although this
conclusion was obtained in a unique environment, we suggest its implications
can also shed light on other similar interesting areas, such as dialog and
conversation, group discussion, and social media.
DECOrrelated feature space partitioning for distributed sparse regression
Authors: Xiangyu Wang, David Dunson, Chenlei Leng
Date: 2016-02-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: parameter estimation, regression, Variable Selection

Fitting statistical models is computationally challenging when the sample
size or the dimension of the dataset is huge. An attractive approach for
down-scaling the problem size is to first partition the dataset into subsets
and then fit using distributed algorithms. The dataset can be partitioned
either horizontally (in the sample space) or vertically (in the feature space).
While the majority of the literature focuses on sample space partitioning,
feature space partitioning is more effective when $p\gg n$. Existing methods
for partitioning features, however, are either vulnerable to high correlations
or inefficient in reducing the model dimension. In this paper, we solve these
problems through a new embarrassingly parallel framework named DECO for
distributed variable selection and parameter estimation. In DECO, variables are
first partitioned and allocated to $m$ distributed workers. The decorrelated
subset data within each worker are then fitted via any algorithm designed for
high-dimensional problems. We show that by incorporating the decorrelation
step, DECO can achieve consistent variable selection and parameter estimation
on each subset with (almost) no assumptions. In addition, the convergence rate
is nearly minimax optimal for both sparse and weakly sparse models and does NOT
depend on the partition number $m$. Extensive numerical experiments are
provided to illustrate the performance of the new framework.
A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
Authors: Satyanath Bhat, Divya Padmanabhan, Shweta Jain, Y. Narahari
Date: 2016-02-12 00:00:00

We study a problem of allocating divisible jobs, arriving online, to workers
in a crowdsourcing setting which involves learning two parameters of
strategically behaving workers. Each job is split into a certain number of
tasks that are then allocated to workers. Each arriving job has to be completed
within a deadline and each task has to be completed satisfying an upper bound
on probability of failure. The job population is homogeneous while the workers
are heterogeneous in terms of costs, completion times, and times to failure.
The job completion time and time to failure of each worker are stochastic with
fixed but unknown means. The requester is faced with the challenge of learning
two separate parameters of each (strategically behaving) worker simultaneously,
namely, the mean job completion time and the mean time to failure. The time to
failure of a worker depends on the duration of the task handled by the worker.
Assuming non-strategic workers to start with, we solve this biparameter
learning problem by applying the Robust UCB algorithm. Then, we non-trivially
extend this algorithm to the setting where the workers are strategic about
their costs. Our proposed mechanism is dominant strategy incentive compatible
and ex-post individually rational with asymptotically optimal regret
performance.
Energetics of the brain and AI
Authors: Anders Sandberg
Date: 2016-02-12 00:00:00

Does the energy requirements for the human brain give energy constraints that
give reason to doubt the feasibility of artificial intelligence? This report
will review some relevant estimates of brain bioenergetics and analyze some of
the methods of estimating brain emulation energy requirements. Turning to AI,
there are reasons to believe the energy requirements for de novo AI to have
little correlation with brain (emulation) energy requirements since cost could
depend merely of the cost of processing higher-level representations rather
than billions of neural firings. Unless one thinks the human way of thinking is
the most optimal or most easily implementable way of achieving software
intelligence, we should expect de novo AI to make use of different, potentially
very compressed and fast, processes.
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Authors: Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis, Konstantinos D. Koutroumbas
Date: 2016-02-11 00:00:00
Tasks: Dictionary Learning

Extracting the underlying low-dimensional space where high-dimensional
signals often reside has long been at the center of numerous algorithms in the
signal processing and machine learning literature during the past few decades.
At the same time, working with incomplete (partly observed) large scale
datasets has recently been commonplace for diverse reasons. This so called {\it
big data era} we are currently living calls for devising online subspace
learning algorithms that can suitably handle incomplete data. Their envisaged
objective is to {\it recursively} estimate the unknown subspace by processing
streaming data sequentially, thus reducing computational complexity, while
obviating the need for storing the whole dataset in memory. In this paper, an
online variational Bayes subspace learning algorithm from partial observations
is presented. To account for the unawareness of the true rank of the subspace,
commonly met in practice, low-rankness is explicitly imposed on the sought
subspace data matrix by exploiting sparse Bayesian learning principles.
Moreover, sparsity, {\it simultaneously} to low-rankness, is favored on the
subspace matrix by the sophisticated hierarchical Bayesian scheme that is
adopted. In doing so, the proposed algorithm becomes adept in dealing with
applications whereby the underlying subspace may be also sparse, as, e.g., in
sparse dictionary learning problems. As shown, the new subspace tracking scheme
outperforms its state-of-the-art counterparts in terms of estimation accuracy,
in a variety of experiments conducted on simulated and real data.
An automatic method for segmentation of fission tracks in epidote crystal photomicrographs
Authors: Alexandre Fioravante de Siqueira, Wagner Massayuki Nakasuga, Aylton Pagamisse, Carlos Alberto Tello Saenz, Aldo Eloizo Job
Date: 2016-02-12 00:00:00

Manual identification of fission tracks has practical problems, such as
variation due to observer-observation efficiency. An automatic processing
method that could identify fission tracks in a photomicrograph could solve this
problem and improve the speed of track counting. However, separation of
non-trivial images is one of the most difficult tasks in image processing.
Several commercial and free softwares are available, but these softwares are
meant to be used in specific images. In this paper, an automatic method based
on starlet wavelets is presented in order to separate fission tracks in mineral
photomicrographs. Automatization is obtained by Matthews correlation
coefficient, and results are evaluated by precision, recall and accuracy. This
technique is an improvement of a method aimed at segmentation of scanning
electron microscopy images. This method is applied in photomicrographs of
epidote phenocrystals, in which accuracy higher than 89% was obtained in
fission track segmentation, even for difficult images. Algorithms corresponding
to the proposed method are available for download. Using the method presented
here, an user could easily determine fission tracks in photomicrographs of
mineral samples.
Stereo Matching by Joint Energy Minimization
Authors: Hongyang Xue, Deng Cai
Date: 2016-01-15 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

In [18], Mozerov et al. propose to perform stereo matching as a two-step
energy minimization problem. For the first step they solve a fully connected
MRF model. And in the next step the marginal output is employed as the unary
cost for a locally connected MRF model.
  In this paper we intend to combine the two steps of energy minimization in
order to improve stereo matching results. We observe that the fully connected
MRF leads to smoother disparity maps, while the locally connected MRF achieves
superior results in fine-structured regions. Thus we propose to jointly solve
the fully connected and locally connected models, taking both their advantages
into account. The joint model is solved by mean field approximations. While
remaining efficient, our joint model outperforms the two-step energy
minimization approach in both time and estimation error on the Middlebury
stereo benchmark v3.
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation
Authors: Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar
Date: 2016-02-12 00:00:00

The problem of estimating sparse eigenvectors of a symmetric matrix attracts
a lot of attention in many applications, especially those with high dimensional
data set. While classical eigenvectors can be obtained as the solution of a
maximization problem, existing approaches formulate this problem by adding a
penalty term into the objective function that encourages a sparse solution.
However, the resulting methods achieve sparsity at the expense of sacrificing
the orthogonality property. In this paper, we develop a new method to estimate
dominant sparse eigenvectors without trading off their orthogonality. The
problem is highly non-convex and hard to handle. We apply the MM framework
where we iteratively maximize a tight lower bound (surrogate function) of the
objective function over the Stiefel manifold. The inner maximization problem
turns out to be a rectangular Procrustes problem, which has a closed form
solution. In addition, we propose a method to improve the covariance estimation
problem when its underlying eigenvectors are known to be sparse. We use the
eigenvalue decomposition of the covariance matrix to formulate an optimization
problem where we impose sparsity on the corresponding eigenvectors. Numerical
experiments show that the proposed eigenvector extraction algorithm matches or
outperforms existing algorithms in terms of support recovery and explained
variance, while the covariance estimation algorithms improve significantly the
sample covariance estimator.