pwc-archive/papers-with-abstracts huggingface dataset:
Automatic LQR Tuning Based on Gaussian Process Global Optimization
Authors: Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe
Date: 2016-05-06 00:00:00
Tasks: Bayesian Optimization, global-optimization

This paper proposes an automatic controller tuning framework based on linear
optimal control combined with Bayesian optimization. With this framework, an
initial set of controller gains is automatically improved according to a
pre-defined performance objective evaluated from experimental data. The
underlying Bayesian optimization algorithm is Entropy Search, which represents
the latent objective as a Gaussian process and constructs an explicit belief
over the location of the objective minimum. This is used to maximize the
information gain from each experimental evaluation. Thus, this framework shall
yield improved controllers with fewer evaluations compared to alternative
approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is
used as the experimental demonstrator. Results of a two- and four-dimensional
tuning problems highlight the method's potential for automatic controller
tuning on robotic platforms.
Energy Disaggregation for Real-Time Building Flexibility Detection
Authors: Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
Date: 2016-05-06 00:00:00
Tasks: energy management, Management

Energy is a limited resource which has to be managed wisely, taking into
account both supply-demand matching and capacity constraints in the
distribution grid. One aspect of the smart energy management at the building
level is given by the problem of real-time detection of flexible demand
available. In this paper we propose the use of energy disaggregation techniques
to perform this task. Firstly, we investigate the use of existing
classification methods to perform energy disaggregation. A comparison is
performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors,
Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted
Boltzmann Machine to automatically perform feature extraction. The extracted
features are then used as inputs to the four classifiers and consequently shown
to improve their accuracy. The efficiency of our approach is demonstrated on a
real database consisting of detailed appliance-level measurements with high
temporal resolution, which has been used for energy disaggregation in previous
studies, namely the REDD. The results show robustness and good generalization
capabilities to newly presented buildings with at least 96% accuracy.
UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction
Authors: Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof
Date: 2016-05-06 00:00:00
Tasks: 3D Reconstruction

In this paper we present an autonomous system for acquiring close-range
high-resolution images that maximize the quality of a later-on 3D
reconstruction with respect to coverage, ground resolution and 3D uncertainty.
In contrast to previous work, our system uses the already acquired images to
predict the confidence in the output of a dense multi-view stereo approach
without executing it. This confidence encodes the likelihood of a successful
reconstruction with respect to the observed scene and potential camera
constellations. Our prediction module runs in real-time and can be trained
without any externally recorded ground truth. We use the confidence prediction
for on-site quality assurance and for planning further views that are tailored
for a specific multi-view stereo approach with respect to the given scene. We
demonstrate the capabilities of our approach with an autonomous Unmanned Aerial
Vehicle (UAV) in a challenging outdoor scenario.
User Reviews and Language: How Language Influences Ratings
Authors: Scott A. Hale
Date: 2016-05-06 00:00:00

The number of user reviews of tourist attractions, restaurants, mobile apps,
etc. is increasing for all languages; yet, research is lacking on how reviews
in multiple languages should be aggregated and displayed. Speakers of different
languages may have consistently different experiences, e.g., different
information available in different languages at tourist attractions or
different user experiences with software due to
internationalization/localization choices. This paper assesses the similarity
in the ratings given by speakers of different languages to London tourist
attractions on TripAdvisor. The correlations between different languages are
generally high, but some language pairs are more correlated than others. The
results question the common practice of computing average ratings from reviews
in many languages.
Market forecasting using Hidden Markov Models
Authors: Sara Rebagliati, Emanuela Sasso, Samuele Soraggi
Date: 2015-04-29 00:00:00
Tasks: Time Series, Time Series Analysis

Working on the daily closing prices and logreturns, in this paper we deal
with the use of Hidden Markov Models (HMMs) to forecast the price of the
EUR/USD Futures. The aim of our work is to understand how the HMMs describe
different financial time series depending on their structure. Subsequently, we
analyse the forecasting methods exposed in the previous literature, putting on
evidence their pros and cons.
Notes on Low-rank Matrix Factorization
Authors: Yuan Lu, Jie Yang
Date: 2015-06-30 00:00:00
Tasks: Clustering, Dimensionality Reduction, Matrix Completion

Low-rank matrix factorization (MF) is an important technique in data science.
The key idea of MF is that there exists latent structures in the data, by
uncovering which we could obtain a compressed representation of the data. By
factorizing an original matrix to low-rank matrices, MF provides a unified
method for dimension reduction, clustering, and matrix completion. In this
article we review several important variants of MF, including: Basic MF,
Non-negative MF, Orthogonal non-negative MF. As can be told from their names,
non-negative MF and orthogonal non-negative MF are variants of basic MF with
non-negativity and/or orthogonality constraints. Such constraints are useful in
specific senarios. In the first part of this article, we introduce, for each of
these models, the application scenarios, the distinctive properties, and the
optimizing method. By properly adapting MF, we can go beyond the problem of
clustering and matrix completion. In the second part of this article, we will
extend MF to sparse matrix compeletion, enhance matrix compeletion using
various regularization methods, and make use of MF for (semi-)supervised
learning by introducing latent space reinforcement and transformation. We will
see that MF is not only a useful model but also as a flexible framework that is
applicable for various prediction problems.
Resource allocation using metaheuristic search
Authors: Andy M. Connor, Amit Shah
Date: 2016-05-06 00:00:00
Tasks: Management, Scheduling

This research is focused on solving problems in the area of software project
management using metaheuristic search algorithms and as such is research in the
field of search based software engineering. The main aim of this research is to
evaluate the performance of different metaheuristic search techniques in
resource allocation and scheduling problems that would be typical of software
development projects. This paper reports a set of experiments which evaluate
the performance of three algorithms, namely simulated annealing, tabu search
and genetic algorithms. The experimental results indicate that all of the
metaheuristics search techniques can be used to solve problems in resource
allocation and scheduling within a software project. Finally, a comparative
analysis suggests that overall the genetic algorithm had performed better than
simulated annealing and tabu search.
The KB paradigm and its application to interactive configuration
Authors: Pieter Van Hertum, Ingmar Dasseville, Gerda Janssens, Marc Denecker
Date: 2016-05-06 00:00:00

The knowledge base paradigm aims to express domain knowledge in a rich formal
language, and to use this domain knowledge as a knowledge base to solve various
problems and tasks that arise in the domain by applying multiple forms of
inference. As such, the paradigm applies a strict separation of concerns
between information and problem solving. In this paper, we analyze the
principles and feasibility of the knowledge base paradigm in the context of an
important class of applications: interactive configuration problems. In
interactive configuration problems, a configuration of interrelated objects
under constraints is searched, where the system assists the user in reaching an
intended configuration. It is widely recognized in industry that good software
solutions for these problems are very difficult to develop. We investigate such
problems from the perspective of the KB paradigm. We show that multiple
functionalities in this domain can be achieved by applying different forms of
logical inferences on a formal specification of the configuration domain. We
report on a proof of concept of this approach in a real-life application with a
banking company. To appear in Theory and Practice of Logic Programming (TPLP).
Detecting Context Dependence in Exercise Item Candidates Selected from Corpora
Authors: Ildikó Pilán
Date: 2016-05-06 00:00:00

We explore the factors influencing the dependence of single sentences on
their larger textual context in order to automatically identify candidate
sentences for language learning exercises from corpora which are presentable in
isolation. An in-depth investigation of this question has not been previously
carried out. Understanding this aspect can contribute to a more efficient
selection of candidate sentences which, besides reducing the time required for
item writing, can also ensure a higher degree of variability and authenticity.
We present a set of relevant aspects collected based on the qualitative
analysis of a smaller set of context-dependent corpus example sentences.
Furthermore, we implemented a rule-based algorithm using these criteria which
achieved an average precision of 0.76 for the identification of different
issues related to context dependence. The method has also been evaluated
empirically where 80% of the sentences in which our system did not detect
context-dependent elements were also considered context-independent by human
raters.
Perceptually Consistent Color-to-Gray Image Conversion
Authors: Shaodi You, Nick Barnes, Janine Walker
Date: 2016-05-06 00:00:00

In this paper, we propose a color to grayscale image conversion algorithm
(C2G) that aims to preserve the perceptual properties of the color image as
much as possible. To this end, we propose measures for two perceptual
properties based on contemporary research in vision science: brightness and
multi-scale contrast. The brightness measurement is based on the idea that the
brightness of a grayscale image will affect the perception of the probability
of color information. The color contrast measurement is based on the idea that
the contrast of a given pixel to its surroundings can be measured as a linear
combination of color contrast at different scales. Based on these measures we
propose a graph based optimization framework to balance the brightness and
contrast measurements. To solve the optimization, an $\ell_1$-norm based method
is provided which converts color discontinuities to brightness discontinuities.
To validate our methods, we evaluate against the existing \cadik and Color250
datasets, and against NeoColor, a new dataset that improves over existing C2G
datasets. NeoColor contains around 300 images from typical C2G scenarios,
including: commercial photograph, printing, books, magazines, masterpiece
artworks and computer designed graphics. We show improvements in metrics of
performance, and further through a user study, we validate the performance of
both the algorithm and the metric.
Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals
Authors: Gao Zhu, Fatih Porikli, Hongdong Li
Date: 2016-05-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, valid

Most tracking-by-detection methods employ a local search window around the
predicted object location in the current frame assuming the previous location
is accurate, the trajectory is smooth, and the computational capacity permits a
search radius that can accommodate the maximum speed yet small enough to reduce
mismatches. These, however, may not be valid always, in particular for fast and
irregularly moving objects. Here, we present an object tracker that is not
limited to a local search window and has ability to probe efficiently the
entire frame. Our method generates a small number of "high-quality" proposals
by a novel instance-specific objectness measure and evaluates them against the
object model that can be adopted from an existing tracking-by-detection
approach as a core tracker. During the tracking process, we update the object
model concentrating on hard false-positives supplied by the proposals, which
help suppressing distractors caused by difficult background clutters, and learn
how to re-rank proposals according to the object model. Since we reduce
significantly the number of hypotheses the core tracker evaluates, we can use
richer object descriptors and stronger detector. Our method outperforms most
recent state-of-the-art trackers on popular tracking benchmarks, and provides
improved robustness for fast moving objects as well as for ultra low-frame-rate
videos.
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Authors: Feng Wang, Huichao Gong, Gaochao liu, Meijing Li, Chuangye Yan, Tian Xia, Xueming Li, Jianyang Zeng
Date: 2016-05-06 00:00:00
Tasks: Single Particle Analysis

Particle picking is a time-consuming step in single-particle analysis and
often requires significant interventions from users, which has become a
bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we
report a deep learning framework, called DeepPicker, to address this problem
and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker
employs a novel cross-molecule training strategy to capture common features of
particles from previously-analyzed micrographs, and thus does not require any
human intervention during particle picking. Tests on the recently-published
cryo-EM data of three complexes have demonstrated that our deep learning based
scheme can successfully accomplish the human-level particle picking process and
identify a sufficient number of particles that are comparable to those manually
by human experts. These results indicate that DeepPicker can provide a
practically useful tool to significantly reduce the time and manual effort
spent in single-particle analysis and thus greatly facilitate high-resolution
cryo-EM structure determination.
Cross-Graph Learning of Multi-Relational Associations
Authors: Hanxiao Liu, Yiming Yang
Date: 2016-05-06 00:00:00
Tasks: Graph Learning, Relational Reasoning, Transductive Learning

Cross-graph Relational Learning (CGRL) refers to the problem of predicting
the strengths or labels of multi-relational tuples of heterogeneous object
types, through the joint inference over multiple graphs which specify the
internal connections among each type of objects. CGRL is an open challenge in
machine learning due to the daunting number of all possible tuples to deal with
when the numbers of nodes in multiple graphs are large, and because the labeled
training instances are extremely sparse as typical. Existing methods such as
tensor factorization or tensor-kernel machines do not work well because of the
lack of convex formulation for the optimization of CGRL models, the poor
scalability of the algorithms in handling combinatorial numbers of tuples,
and/or the non-transductive nature of the learning methods which limits their
ability to leverage unlabeled data in training. This paper proposes a novel
framework which formulates CGRL as a convex optimization problem, enables
transductive learning using both labeled and unlabeled tuples, and offers a
scalable algorithm that guarantees the optimal solution and enjoys a linear
time complexity with respect to the sizes of input graphs. In our experiments
with a subset of DBLP publication records and an Enzyme multi-source dataset,
the proposed method successfully scaled to the large cross-graph inference
problem, and outperformed other representative approaches significantly.
Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection
Authors: Jiaolong Yang, Hongdong Li, Yuchao Dai, Robby T. Tan
Date: 2016-05-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, valid

This paper deals with a challenging, frequently encountered, yet not properly
investigated problem in two-frame optical flow estimation. That is, the input
frames are compounds of two imaging layers -- one desired background layer of
the scene, and one distracting, possibly moving layer due to transparency or
reflection. In this situation, the conventional brightness constancy constraint
-- the cornerstone of most existing optical flow methods -- will no longer be
valid. In this paper, we propose a robust solution to this problem. The
proposed method performs both optical flow estimation, and image layer
separation. It exploits a generalized double-layer brightness consistency
constraint connecting these two tasks, and utilizes the priors for both of
them. Experiments on both synthetic data and real images have confirmed the
efficacy of the proposed method. To the best of our knowledge, this is the
first attempt towards handling generic optical flow fields of two-frame images
containing transparency or reflection.
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity
Authors: Sohil Shah, Tom Goldstein, Christoph Studer
Date: 2016-05-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Restoration

Conventional algorithms for sparse signal recovery and sparse representation
rely on $l_1$-norm regularized variational methods. However, when applied to
the reconstruction of $\textit{sparse images}$, i.e., images where only a few
pixels are non-zero, simple $l_1$-norm-based methods ignore potential
correlations in the support between adjacent pixels. In a number of
applications, one is interested in images that are not only sparse, but also
have a support with smooth (or contiguous) boundaries. Existing algorithms that
take into account such a support structure mostly rely on non-convex methods
and---as a consequence---do not scale well to high-dimensional problems and/or
do not converge to global optima. In this paper, we explore the use of new
block $l_1$-norm regularizers, which enforce image sparsity while
simultaneously promoting smooth support structure. By exploiting the convexity
of our regularizers, we develop new computationally-efficient recovery
algorithms that guarantee global optimality. We demonstrate the efficacy of our
regularizers on a variety of imaging tasks including compressive image
recovery, image restoration, and robust PCA.
Information Recovery from Pairwise Measurements
Authors: Yuxin Chen, Changho Suh, Andrea J. Goldsmith
Date: 2015-04-06 00:00:00
Tasks: Stochastic Block Model

This paper is concerned with jointly recovering $n$ node-variables $\left\{
x_{i}\right\}_{1\leq i\leq n}$ from a collection of pairwise difference
measurements. Imagine we acquire a few observations taking the form of
$x_{i}-x_{j}$; the observation pattern is represented by a measurement graph
$\mathcal{G}$ with an edge set $\mathcal{E}$ such that $x_{i}-x_{j}$ is
observed if and only if $(i,j)\in\mathcal{E}$. To account for noisy
measurements in a general manner, we model the data acquisition process by a
set of channels with given input/output transition measures. Employing
information-theoretic tools applied to channel decoding problems, we develop a
\emph{unified} framework to characterize the fundamental recovery criterion,
which accommodates general graph structures, alphabet sizes, and channel
transition measures. In particular, our results isolate a family of
\emph{minimum} \emph{channel divergence measures} to characterize the degree of
measurement corruption, which together with the size of the minimum cut of
$\mathcal{G}$ dictates the feasibility of exact information recovery. For
various homogeneous graphs, the recovery condition depends almost only on the
edge sparsity of the measurement graph irrespective of other graphical metrics;
alternatively, the minimum sample complexity required for these graphs scales
like \[ \text{minimum sample complexity }\asymp\frac{n\log
n}{\mathsf{Hel}_{1/2}^{\min}} \] for certain information metric
$\mathsf{Hel}_{1/2}^{\min}$ defined in the main text, as long as the alphabet
size is not super-polynomial in $n$. We apply our general theory to three
concrete applications, including the stochastic block model, the outlier model,
and the haplotype assembly problem. Our theory leads to order-wise tight
recovery conditions for all these scenarios.
Robust SAR STAP via Kronecker Decomposition
Authors: Kristjan Greenewald, Edmund Zelnio, Alfred Hero
Date: 2016-05-05 00:00:00

This paper proposes a spatio-temporal decomposition for the detection of
moving targets in multiantenna SAR. As a high resolution radar imaging
modality, SAR detects and localizes non-moving targets accurately, giving it an
advantage over lower resolution GMTI radars. Moving target detection is more
challenging due to target smearing and masking by clutter. Space-time adaptive
processing (STAP) is often used to remove the stationary clutter and enhance
the moving targets. In this work, it is shown that the performance of STAP can
be improved by modeling the clutter covariance as a space vs. time Kronecker
product with low rank factors. Based on this model, a low-rank Kronecker
product covariance estimation algorithm is proposed, and a novel separable
clutter cancelation filter based on the Kronecker covariance estimate is
introduced. The proposed method provides orders of magnitude reduction in the
required number of training samples, as well as improved robustness to
corruption of the training data. Simulation results and experiments using the
Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of
our approach relative to existing techniques.
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
Authors: Brendan Alvey, Alina Zare, Matthew Cook, Dominic K. Ho
Date: 2016-03-19 00:00:00

The adaptive coherence estimator (ACE) estimates the squared cosine of the
angle between a known target vector and a sample vector in a whitened
coordinate space. The space is whitened according to an estimation of the
background statistics, which directly effects the performance of the statistic
as a target detector. In this paper, the ACE detection statistic is used to
detect buried explosive hazards with data from a Wideband Electromagnetic
Induction (WEMI) sensor. Target signatures are based on a dictionary defined
using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are
summarized as a receiver operator curve (ROC) and compared to other leading
methods.