pwc-archive/papers-with-abstracts huggingface dataset:
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
Authors: Vijay Manikandan Janakiraman, XuanLong Nguyen, Dennis Assanis
Date: 2015-01-16 00:00:00
Tasks: Model Predictive Control

Homogeneous charge compression ignition (HCCI) is a futuristic combustion
technology that operates with a high fuel efficiency and reduced emissions.
HCCI combustion is characterized by complex nonlinear dynamics which
necessitates a model based control approach for automotive application. HCCI
engine control is a nonlinear, multi-input multi-output problem with state and
actuator constraints which makes controller design a challenging task. Typical
HCCI controllers make use of a first principles based model which involves a
long development time and cost associated with expert labor and calibration. In
this paper, an alternative approach based on machine learning is presented
using extreme learning machines (ELM) and nonlinear model predictive control
(MPC). A recurrent ELM is used to learn the nonlinear dynamics of HCCI engine
using experimental data and is shown to accurately predict the engine behavior
several steps ahead in time, suitable for predictive control. Using the ELM
engine models, an MPC based control algorithm with a simplified quadratic
program update is derived for real time implementation. The working and
effectiveness of the MPC approach has been analyzed on a nonlinear HCCI engine
model for tracking multiple reference quantities along with constraints defined
by HCCI states, actuators and operational limits.
Value Iteration with Options and State Aggregation
Authors: Kamil Ciosek, David Silver
Date: 2015-01-16 00:00:00

This paper presents a way of solving Markov Decision Processes that combines
state abstraction and temporal abstraction. Specifically, we combine state
aggregation with the options framework and demonstrate that they work well
together and indeed it is only after one combines the two that the full benefit
of each is realized. We introduce a hierarchical value iteration algorithm
where we first coarsely solve subgoals and then use these approximate solutions
to exactly solve the MDP. This algorithm solved several problems faster than
vanilla value iteration.
Mind the Gap: Subspace based Hierarchical Domain Adaptation
Authors: Anant Raj, Vinay P. Namboodiri, Tinne Tuytelaars
Date: 2015-01-16 00:00:00
Tasks: Domain Adaptation

Domain adaptation techniques aim at adapting a classifier learnt on a source
domain to work on the target domain. Exploiting the subspaces spanned by
features of the source and target domains respectively is one approach that has
been investigated towards solving this problem. These techniques normally
assume the existence of a single subspace for the entire source / target
domain. In this work, we consider the hierarchical organization of the data and
consider multiple subspaces for the source and target domain based on the
hierarchy. We evaluate different subspace based domain adaptation techniques
under this setting and observe that using different subspaces based on the
hierarchy yields consistent improvement over a non-hierarchical baseline
Multi-view learning for multivariate performance measures optimization
Authors: Jim Jing-Yan Wang
Date: 2015-01-15 00:00:00
Tasks: MULTI-VIEW LEARNING

In this paper, we propose the problem of optimizing multivariate performance
measures from multi-view data, and an effective method to solve it. This
problem has two features: the data points are presented by multiple views, and
the target of learning is to optimize complex multivariate performance
measures. We propose to learn a linear discriminant functions for each view,
and combine them to construct a overall multivariate mapping function for
mult-view data. To learn the parameters of the linear dis- criminant functions
of different views to optimize multivariate performance measures, we formulate
a optimization problem. In this problem, we propose to minimize the complexity
of the linear discriminant functions of each view, encourage the consistences
of the responses of different views over the same data points, and minimize the
upper boundary of a given multivariate performance measure. To optimize this
problem, we employ the cutting-plane method in an iterative algorithm. In each
iteration, we update a set of constrains, and optimize the mapping function
parameter of each view one by one.
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation
Authors: Sabina Tangaro, Nicola Amoroso, Massimo Brescia, Stefano Cavuoti, Andrea Chincarini, Rosangela Errico, Paolo Inglese, Giuseppe Longo, Rosalia Maglietta, Andrea Tateo, Giuseppe Riccio, Roberto Bellotti
Date: 2015-01-16 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Hippocampus

Neurodegenerative diseases are frequently associated with structural changes
in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations
and therefore be used as a supportive feature for a number of neurodegenerative
diseases. The hippocampus has been known to be a biomarker for Alzheimer
disease and other neurological and psychiatric diseases. However, it requires
accurate, robust and reproducible delineation of hippocampal structures. Fully
automatic methods are usually the voxel based approach, for each voxel a number
of local features were calculated. In this paper we compared four different
techniques for feature selection from a set of 315 features extracted for each
voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper
methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential
Backward Elimination; and (iv) embedded method based on the Random Forest
Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent
set of 25 subjects. The resulting segmentations were compared with manual
reference labelling. By using only 23 features for each voxel (sequential
backward elimination) we obtained comparable state of-the-art performances with
respect to the standard tool FreeSurfer.
Prediction and Modularity in Dynamical Systems
Authors: Artemy Kolchinsky, Luis M. Rocha
Date: 2011-06-19 00:00:00
Tasks: Prediction

Identifying and understanding modular organizations is centrally important in
the study of complex systems. Several approaches to this problem have been
advanced, many framed in information-theoretic terms. Our treatment starts from
the complementary point of view of statistical modeling and prediction of
dynamical systems. It is known that for finite amounts of training data,
simpler models can have greater predictive power than more complex ones. We use
the trade-off between model simplicity and predictive accuracy to generate
optimal multiscale decompositions of dynamical networks into weakly-coupled,
simple modules. State-dependent and causal versions of our method are also
proposed.
A new ADMM algorithm for the Euclidean median and its application to robust patch regression
Authors: Kunal. N. Chaudhury, K. R. Ramakrishnan
Date: 2015-01-16 00:00:00
Tasks: Denoising, Image Denoising, regression

The Euclidean Median (EM) of a set of points $\Omega$ in an Euclidean space
is the point x minimizing the (weighted) sum of the Euclidean distances of x to
the points in $\Omega$. While there exits no closed-form expression for the EM,
it can nevertheless be computed using iterative methods such as the Wieszfeld
algorithm. The EM has classically been used as a robust estimator of centrality
for multivariate data. It was recently demonstrated that the EM can be used to
perform robust patch-based denoising of images by generalizing the popular
Non-Local Means algorithm. In this paper, we propose a novel algorithm for
computing the EM (and its box-constrained counterpart) using variable splitting
and the method of augmented Lagrangian. The attractive feature of this approach
is that the subproblems involved in the ADMM-based optimization of the
augmented Lagrangian can be resolved using simple closed-form projections. The
proposed ADMM solver is used for robust patch-based image denoising and is
shown to exhibit faster convergence compared to an existing solver.
Bayesian Nonparametrics in Topic Modeling: A Brief Tutorial
Authors: Alexander Spangher
Date: 2015-01-16 00:00:00

Using nonparametric methods has been increasingly explored in Bayesian
hierarchical modeling as a way to increase model flexibility. Although the
field shows a lot of promise, inference in many models, including Hierachical
Dirichlet Processes (HDP), remain prohibitively slow. One promising path
forward is to exploit the submodularity inherent in Indian Buffet Process (IBP)
to derive near-optimal solutions in polynomial time. In this work, I will
present a brief tutorial on Bayesian nonparametric methods, especially as they
are applied to topic modeling. I will show a comparison between different
non-parametric models and the current state-of-the-art parametric model, Latent
Dirichlet Allocation (LDA).
Stable Camera Motion Estimation Using Convex Programming
Authors: Onur Ozyesil, Amit Singer, Ronen Basri
Date: 2013-12-18 00:00:00
Tasks: Clustering, Motion Estimation, Negation

We study the inverse problem of estimating n locations $t_1, ..., t_n$ (up to
global scale, translation and negation) in $R^d$ from noisy measurements of a
subset of the (unsigned) pairwise lines that connect them, that is, from noisy
measurements of $\pm (t_i - t_j)/\|t_i - t_j\|$ for some pairs (i,j) (where the
signs are unknown). This problem is at the core of the structure from motion
(SfM) problem in computer vision, where the $t_i$'s represent camera locations
in $R^3$. The noiseless version of the problem, with exact line measurements,
has been considered previously under the general title of parallel rigidity
theory, mainly in order to characterize the conditions for unique realization
of locations. For noisy pairwise line measurements, current methods tend to
produce spurious solutions that are clustered around a few locations. This
sensitivity of the location estimates is a well-known problem in SfM,
especially for large, irregular collections of images.
  In this paper we introduce a semidefinite programming (SDP) formulation,
specially tailored to overcome the clustering phenomenon. We further identify
the implications of parallel rigidity theory for the location estimation
problem to be well-posed, and prove exact (in the noiseless case) and stable
location recovery results. We also formulate an alternating direction method to
solve the resulting semidefinite program, and provide a distributed version of
our formulation for large numbers of locations. Specifically for the camera
location estimation problem, we formulate a pairwise line estimation method
based on robust camera orientation and subspace estimation. Lastly, we
demonstrate the utility of our algorithm through experiments on real images.
PAC-Bayes with Minimax for Confidence-Rated Transduction
Authors: Akshay Balsubramani, Yoav Freund
Date: 2015-01-15 00:00:00
Tasks: Prediction

We consider using an ensemble of binary classifiers for transductive
prediction, when unlabeled test data are known in advance. We derive minimax
optimal rules for confidence-rated prediction in this setting. By using
PAC-Bayes analysis on these rules, we obtain data-dependent performance
guarantees without distributional assumptions on the data. Our analysis
techniques are readily extended to a setting in which the predictor is allowed
to abstain.
Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Authors: Vince Lyzinski, Daniel Sussman, Minh Tang, Avanti Athreya, Carey Priebe
Date: 2013-10-02 00:00:00
Tasks: Clustering

Vertex clustering in a stochastic blockmodel graph has wide applicability and
has been the subject of extensive research. In thispaper, we provide a short
proof that the adjacency spectral embedding can be used to obtain perfect
clustering for the stochastic blockmodel and the degree-corrected stochastic
blockmodel. We also show an analogous result for the more general random dot
product graph model.
The Fast Convergence of Incremental PCA
Authors: Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
Date: 2015-01-15 00:00:00
Proceeding: NeurIPS 2013 12

We consider a situation in which we see samples in $\mathbb{R}^d$ drawn
i.i.d. from some distribution with mean zero and unknown covariance A. We wish
to compute the top eigenvector of A in an incremental fashion - with an
algorithm that maintains an estimate of the top eigenvector in O(d) space, and
incrementally adjusts the estimate with each new data point that arrives. Two
classical such schemes are due to Krasulina (1969) and Oja (1983). We give
finite-sample convergence rates for both.
Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
Authors: Denis Kleyko, Evgeny Osipov, Alexander Senior, Asad I. Khan, Y. Ahmet Şekercioğlu
Date: 2015-01-15 00:00:00

This article proposes the use of Vector Symbolic Architectures for
implementing Hierarchical Graph Neuron, an architecture for memorizing patterns
of generic sensor stimuli. The adoption of a Vector Symbolic representation
ensures a one-layered design for the approach, while maintaining the previously
reported properties and performance characteristics of Hierarchical Graph
Neuron, and also improving the noise resistance of the architecture. The
proposed architecture enables a linear (with respect to the number of stored
entries) time search for an arbitrary sub-pattern.
Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study
Authors: Holger R. Roth, Thomas E. Hampshire, Emma Helbren, Mingxing Hu, Roser Vega, Steve Halligan, David J. Hawkes
Date: 2015-01-15 00:00:00
Tasks: 3D Reconstruction

Potentially precancerous polyps detected with CT colonography (CTC) need to
be removed subsequently, using an optical colonoscope (OC). Due to large
colonic deformations induced by the colonoscope, even very experienced
colonoscopists find it difficult to pinpoint the exact location of the
colonoscope tip in relation to polyps reported on CTC. This can cause unduly
prolonged OC examinations that are stressful for the patient, colonoscopist and
supporting staff.
  We developed a method, based on monocular 3D reconstruction from OC images,
that automatically matches polyps observed in OC with polyps reported on prior
CTC. A matching cost is computed, using rigid point-based registration between
surface point clouds extracted from both modalities. A 3D printed and painted
phantom of a 25 cm long transverse colon segment was used to validate the
method on two medium sized polyps. Results indicate that the matching cost is
smaller at the correct corresponding polyp between OC and CTC: the value is 3.9
times higher at the incorrect polyp, comparing the correct match between polyps
to the incorrect match. Furthermore, we evaluate the matching of the
reconstructed polyp from OC with other colonic endoluminal surface structures
such as haustral folds and show that there is a minimum at the correct polyp
from CTC.
  Automated matching between polyps observed at OC and prior CTC would
facilitate the biopsy or removal of true-positive pathology or exclusion of
false-positive CTC findings, and would reduce colonoscopy false-negative
(missed) polyps. Ultimately, such a method might reduce healthcare costs,
patient inconvenience and discomfort.
Submodular relaxation for inference in Markov random fields
Authors: Anton Osokin, Dmitry Vetrov
Date: 2015-01-15 00:00:00

In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.
Visual Analytics of Image-Centric Cohort Studies in Epidemiology
Authors: Bernhard Preim, Paul Klemm, Helwig Hauser, Katrin Hegenscheid, Steffen Oeltze, Klaus Toennies, Henry Völzke
Date: 2015-01-15 00:00:00
Tasks: Epidemiology

Epidemiology characterizes the influence of causes to disease and health
conditions of defined populations. Cohort studies are population-based studies
involving usually large numbers of randomly selected individuals and comprising
numerous attributes, ranging from self-reported interview data to results from
various medical examinations, e.g., blood and urine samples. Since recently,
medical imaging has been used as an additional instrument to assess risk
factors and potential prognostic information. In this chapter, we discuss such
studies and how the evaluation may benefit from visual analytics. Cluster
analysis to define groups, reliable image analysis of organs in medical imaging
data and shape space exploration to characterize anatomical shapes are among
the visual analytics tools that may enable epidemiologists to fully exploit the
potential of their huge and complex data. To gain acceptance, visual analytics
tools need to complement more classical epidemiologic tools, primarily
hypothesis-driven statistical analysis.
Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Authors: Richard J. Preen, Larry Bull
Date: 2013-08-13 00:00:00

The production of renewable and sustainable energy is one of the most
important challenges currently facing mankind. Wind has made an increasing
contribution to the world's energy supply mix, but still remains a long way
from reaching its full potential. In this paper, we investigate the use of
artificial evolution to design vertical-axis wind turbine prototypes that are
physically instantiated and evaluated under fan generated wind conditions.
Initially a conventional evolutionary algorithm is used to explore the design
space of a single wind turbine and later a cooperative coevolutionary algorithm
is used to explore the design space of an array of wind turbines. Artificial
neural networks are used throughout as surrogate models to assist learning and
found to reduce the number of fabrications required to reach a higher
aerodynamic efficiency. Unlike in other approaches, such as computational fluid
dynamics simulations, no mathematical formulations are used and no model
assumptions are made.
LATCH: Learned Arrangements of Three Patch Codes
Authors: Gil Levi, Tal Hassner
Date: 2015-01-15 00:00:00

We present a novel means of describing local image appearances using binary
strings. Binary descriptors have drawn increasing interest in recent years due
to their speed and low memory footprint. A known shortcoming of these
representations is their inferior performance compared to larger, histogram
based descriptors such as the SIFT. Our goal is to close this performance gap
while maintaining the benefits attributed to binary representations. To this
end we propose the Learned Arrangements of Three Patch Codes descriptors, or
LATCH. Our key observation is that existing binary descriptors are at an
increased risk from noise and local appearance variations. This, as they
compare the values of pixel pairs; changes to either of the pixels can easily
lead to changes in descriptor values, hence damaging its performance. In order
to provide more robustness, we instead propose a novel means of comparing pixel
patches. This ostensibly small change, requires a substantial redesign of the
descriptors themselves and how they are produced. Our resulting LATCH
representation is rigorously compared to state-of-the-art binary descriptors
and shown to provide far better performance for similar computation and space
requirements.
Design Mining Interacting Wind Turbines
Authors: Richard J. Preen, Larry Bull
Date: 2014-10-02 00:00:00
Tasks: Evolutionary Algorithms

An initial study of surrogate-assisted evolutionary algorithms used to design
vertical-axis wind turbines wherein candidate prototypes are evaluated under
fan generated wind conditions after being physically instantiated by a 3D
printer has recently been presented. Unlike other approaches, such as
computational fluid dynamics simulations, no mathematical formulations were
used and no model assumptions were made. This paper extends that work by
exploring alternative surrogate modelling and evolutionary techniques. The
accuracy of various modelling algorithms used to estimate the fitness of
evaluated individuals from the initial experiments is compared. The effect of
temporally windowing surrogate model training samples is explored. A
surrogate-assisted approach based on an enhanced local search is introduced;
and alternative coevolution collaboration schemes are examined.
Computational Protein Design Using AND/OR Branch-and-Bound Search
Authors: Yichao Zhou, Yuexin Wu, Jianyang Zeng
Date: 2014-12-08 00:00:00
Tasks: Combinatorial Optimization, Protein Design

The computation of the global minimum energy conformation (GMEC) is an
important and challenging topic in structure-based computational protein
design. In this paper, we propose a new protein design algorithm based on the
AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional
branch-and-bound search algorithm, to solve this combinatorial optimization
problem. By integrating with a powerful heuristic function, AOBB is able to
fully exploit the graph structure of the underlying residue interaction network
of a backbone template to significantly accelerate the design process. Tests on
real protein data show that our new protein design algorithm is able to solve
many prob- lems that were previously unsolvable by the traditional exact search
algorithms, and for the problems that can be solved with traditional provable
algorithms, our new method can provide a large speedup by several orders of
magnitude while still guaranteeing to find the global minimum energy
conformation (GMEC) solution.
Hard to Cheat: A Turing Test based on Answering Questions about Images
Authors: Mateusz Malinowski, Mario Fritz
Date: 2015-01-14 00:00:00
Tasks: Question Answering

Progress in language and image understanding by machines has sparkled the
interest of the research community in more open-ended, holistic tasks, and
refueled an old AI dream of building intelligent machines. We discuss a few
prominent challenges that characterize such holistic tasks and argue for
"question answering about images" as a particular appealing instance of such a
holistic task. In particular, we point out that it is a version of a Turing
Test that is likely to be more robust to over-interpretations and contrast it
with tasks like grounding and generation of descriptions. Finally, we discuss
tools to measure progress in this field.
A Modified No Search Algorithm for Fractal Image Compression
Authors: Mehdi. Salarian, Babak. Mohamadinia, Jalil Rasekhi
Date: 2015-01-13 00:00:00
Tasks: Image Compression

Fractal image compression has some desirable properties like high quality at
high compression ratio, fast decoding, and resolution independence. Therefore
it can be used for many applications such as texture mapping and pattern
recognition and image watermarking. But it suffers from long encoding time due
to its need to find the best match between sub blocks. This time is related to
the approach that is used. In this paper we present a fast encoding Algorithm
based on no search method. Our goal is that more blocks are covered in initial
step of quad tree algorithm. Experimental result has been compared with other
new fast fractal coding methods, showing it is better in term of bit rate in
same condition while the other parameters are fixed.