pwc-archive/papers-with-abstracts huggingface dataset:
Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications
Authors: Avishek Chatterjee, Venu Madhav Govindu
Date: 2015-05-08 00:00:00
Tasks: Denoising

Depth maps obtained from commercially available structured-light stereo based
depth cameras, such as the Kinect, are easy to use but are affected by
significant amounts of noise. This paper is devoted to a study of the intrinsic
noise characteristics of such depth maps, i.e. the standard deviation of noise
in estimated depth varies quadratically with the distance of the object from
the depth camera. We validate this theoretical model against empirical
observations and demonstrate the utility of this noise model in three popular
applications: depth map denoising, volumetric scan merging for 3D modeling, and
identification of 3D planes in depth maps.
Exact and Heuristic Algorithms for Semi-Nonnegative Matrix Factorization
Authors: Nicolas Gillis, Abhishek Kumar
Date: 2014-10-27 00:00:00

Given a matrix $M$ (not necessarily nonnegative) and a factorization rank
$r$, semi-nonnegative matrix factorization (semi-NMF) looks for a matrix $U$
with $r$ columns and a nonnegative matrix $V$ with $r$ rows such that $UV$ is
the best possible approximation of $M$ according to some metric. In this paper,
we study the properties of semi-NMF from which we develop exact and heuristic
algorithms. Our contribution is threefold. First, we prove that the error of a
semi-NMF of rank $r$ has to be smaller than the best unconstrained
approximation of rank $r-1$. This leads us to a new initialization procedure
based on the singular value decomposition (SVD) with a guarantee on the quality
of the approximation. Second, we propose an exact algorithm (that is, an
algorithm that finds an optimal solution), also based on the SVD, for a certain
class of matrices (including nonnegative irreducible matrices) from which we
derive an initialization for matrices not belonging to that class. Numerical
experiments illustrate that this second approach performs extremely well, and
allows us to compute optimal semi-NMF decompositions in many situations.
Finally, we analyze the computational complexity of semi-NMF proving its
NP-hardness, already in the rank-one case (that is, for $r = 1$), and we show
that semi-NMF is sometimes ill-posed (that is, an optimal solution does not
exist).
Towards Formal Fault Tree Analysis using Theorem Proving
Authors: Waqar Ahmed, Osman Hasan
Date: 2015-05-08 00:00:00
Tasks: Automated Theorem Proving

Fault Tree Analysis (FTA) is a dependability analysis technique that has been
widely used to predict reliability, availability and safety of many complex
engineering systems. Traditionally, these FTA-based analyses are done using
paper-and-pencil proof methods or computer simulations, which cannot ascertain
absolute correctness due to their inherent limitations. As a complementary
approach, we propose to use the higher-order-logic theorem prover HOL4 to
conduct the FTA-based analysis of safety-critical systems where accuracy of
failure analysis is a dire need. In particular, the paper presents a
higher-order-logic formalization of generic Fault Tree gates, i.e., AND, OR,
NAND, NOR, XOR and NOT and the formal verification of their failure probability
expressions. Moreover, we have formally verified the generic probabilistic
inclusion-exclusion principle, which is one of the foremost requirements for
conducting the FTA-based failure analysis of any given system. For illustration
purposes, we conduct the FTA-based failure analysis of a solar array that is
used as the main source of power for the Dong Fang Hong-3 (DFH-3) satellite.
Learning to Segment Moving Objects in Videos
Authors: Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen, Jitendra Malik
Date: 2014-12-19 00:00:00
Proceeding: CVPR 2015 6
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

We segment moving objects in videos by ranking spatio-temporal segment
proposals according to "moving objectness": how likely they are to contain a
moving object. In each video frame, we compute segment proposals using multiple
figure-ground segmentations on per frame motion boundaries. We rank them with a
Moving Objectness Detector trained on image and motion fields to detect moving
objects and discard over/under segmentations or background parts of the scene.
We extend the top ranked segments into spatio-temporal tubes using random
walkers on motion affinities of dense point trajectories. Our final tube
ranking consistently outperforms previous segmentation methods in the two
largest video segmentation benchmarks currently available, for any number of
proposals. Further, our per frame moving object proposals increase the
detection rate up to 7\% over previous state-of-the-art static proposal
methods.
PRISM: Person Re-Identification via Structured Matching
Authors: Ziming Zhang, Venkatesh Saligrama
Date: 2014-06-13 00:00:00
Tasks: Graph Matching, Person Re-Identification

Person re-identification (re-id), an emerging problem in visual surveillance,
deals with maintaining entities of individuals whilst they traverse various
locations surveilled by a camera network. From a visual perspective re-id is
challenging due to significant changes in visual appearance of individuals in
cameras with different pose, illumination and calibration. Globally the
challenge arises from the need to maintain structurally consistent matches
among all the individual entities across different camera views. We propose
PRISM, a structured matching method to jointly account for these challenges. We
view the global problem as a weighted graph matching problem and estimate edge
weights by learning to predict them based on the co-occurrences of visual
patterns in the training examples. These co-occurrence based scores in turn
account for appearance changes by inferring likely and unlikely visual
co-occurrences appearing in training instances. We implement PRISM on single
shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in
terms of matching rate while being computationally efficient.
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
Authors: Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, Ruslan Salakhutdinov
Date: 2015-03-13 00:00:00
Tasks: Classification, Event Detection, General Classification, image-classification, Image Classification, Video Classification

We conduct an in-depth exploration of different strategies for doing event
detection in videos using convolutional neural networks (CNNs) trained for
image classification. We study different ways of performing spatial and
temporal pooling, feature normalization, choice of CNN layers as well as choice
of classifiers. Making judicious choices along these dimensions led to a very
significant increase in performance over more naive approaches that have been
used till now. We evaluate our approach on the challenging TRECVID MED'14
dataset with two popular CNN architectures pretrained on ImageNet. On this
MED'14 dataset, our methods, based entirely on image-trained CNN features, can
outperform several state-of-the-art non-CNN models. Our proposed late fusion of
CNN- and motion-based features can further increase the mean average precision
(mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the
state-of-the-art classification performance on the challenging UCF-101 dataset.
Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning
Authors: Darrell Whitley, Renato Tinós, Francisco Chicano
Date: 2015-05-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces the NK Echo State Network. The problem of learning in
the NK Echo State Network is reduced to the problem of optimizing a special
form of a Spin Glass Problem known as an NK Landscape. No weight adjustment is
used; all learning is accomplished by spinning up (turning on) or spinning down
(turning off) neurons in order to find a combination of neurons that work
together to achieve the desired computation. For special types of NK
Landscapes, an exact global solution can be obtained in polynomial time using
dynamic programming. The NK Echo State Network is applied to a reinforcement
learning problem requiring a recurrent network: balancing two poles on a cart
given no velocity information. Empirical results shows that the NK Echo State
Network learns very rapidly and yields very good generalization.
Learning to Search for Dependencies
Authors: Kai-Wei Chang, He He, Hal Daumé III, John Langford
Date: 2015-03-18 00:00:00
Tasks: BIG-bench Machine Learning

We demonstrate that a dependency parser can be built using a credit
assignment compiler which removes the burden of worrying about low-level
machine learning details from the parser implementation. The result is a simple
parser which robustly applies to many languages that provides similar
statistical and computational performance with best-to-date transition-based
parsing approaches, while avoiding various downsides including randomization,
extra feature requirements, and custom learning algorithms.
DART: Dropouts meet Multiple Additive Regression Trees
Authors: K. V. Rashmi, Ran Gilad-Bachrach
Date: 2015-05-07 00:00:00
Tasks: regression

Multiple Additive Regression Trees (MART), an ensemble model of boosted
regression trees, is known to deliver high prediction accuracy for diverse
tasks, and it is widely used in practice. However, it suffers an issue which we
call over-specialization, wherein trees added at later iterations tend to
impact the prediction of only a few instances, and make negligible contribution
towards the remaining instances. This negatively affects the performance of the
model on unseen data, and also makes the model over-sensitive to the
contributions of the few, initially added tress. We show that the commonly used
tool to address this issue, that of shrinkage, alleviates the problem only to a
certain extent and the fundamental issue of over-specialization still remains.
In this work, we explore a different approach to address the problem that of
employing dropouts, a tool that has been recently proposed in the context of
learning deep neural networks. We propose a novel way of employing dropouts in
MART, resulting in the DART algorithm. We evaluate DART on ranking, regression
and classification tasks, using large scale, publicly available datasets, and
show that DART outperforms MART in each of the tasks, with a significant
margin. We also show that DART overcomes the issue of over-specialization to a
considerable extent.
Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics
Authors: Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon
Date: 2015-05-07 00:00:00
Tasks: Binary Classification, Classification, General Classification

We provide a general theoretical analysis of expected out-of-sample utility,
also referred to as decision-theoretic classification, for non-decomposable
binary classification metrics such as F-measure and Jaccard coefficient. Our
key result is that the expected out-of-sample utility for many performance
metrics is provably optimized by a classifier which is equivalent to a signed
thresholding of the conditional probability of the positive class. Our analysis
bridges a gap in the literature on binary classification, revealed in light of
recent results for non-decomposable metrics in population utility maximization
style classification. Our results identify checkable properties of a
performance metric which are sufficient to guarantee a probability ranking
principle. We propose consistent estimators for optimal expected out-of-sample
classification. As a consequence of the probability ranking principle,
computational requirements can be reduced from exponential to cubic complexity
in the general case, and further reduced to quadratic complexity in special
cases. We provide empirical results on simulated and benchmark datasets
evaluating the performance of the proposed algorithms for decision-theoretic
classification and comparing them to baseline and state-of-the-art methods in
population utility maximization for non-decomposable metrics.
Contextual Analysis for Middle Eastern Languages with Hidden Markov Models
Authors: Kazem Taghva
Date: 2015-05-07 00:00:00

Displaying a document in Middle Eastern languages requires contextual
analysis due to different presentational forms for each character of the
alphabet. The words of the document will be formed by the joining of the
correct positional glyphs representing corresponding presentational forms of
the characters. A set of rules defines the joining of the glyphs. As usual,
these rules vary from language to language and are subject to interpretation by
the software developers.
  In this paper, we propose a machine learning approach for contextual analysis
based on the first order Hidden Markov Model. We will design and build a model
for the Farsi language to exhibit this technology. The Farsi model achieves 94
\% accuracy with the training based on a short list of 89 Farsi vocabularies
consisting of 2780 Farsi characters.
  The experiment can be easily extended to many languages including Arabic,
Urdu, and Sindhi. Furthermore, the advantage of this approach is that the same
software can be used to perform contextual analysis without coding complex
rules for each specific language. Of particular interest is that the languages
with fewer speakers can have greater representation on the web, since they are
typically ignored by software developers due to lack of financial incentives.
Fast Spectral Unmixing based on Dykstra's Alternating Projection
Authors: Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret
Date: 2015-05-07 00:00:00

This paper presents a fast spectral unmixing algorithm based on Dykstra's
alternating projection. The proposed algorithm formulates the fully constrained
least squares optimization problem associated with the spectral unmixing task
as an unconstrained regression problem followed by a projection onto the
intersection of several closed convex sets. This projection is achieved by
iteratively projecting onto each of the convex sets individually, following
Dyktra's scheme. The sequence thus obtained is guaranteed to converge to the
sought projection. Thanks to the preliminary matrix decomposition and variable
substitution, the projection is implemented intrinsically in a subspace, whose
dimension is very often much lower than the number of bands. A benefit of this
strategy is that the order of the computational complexity for each projection
is decreased from quadratic to linear time. Numerical experiments considering
diverse spectral unmixing scenarios provide evidence that the proposed
algorithm competes with the state-of-the-art, namely when the number of
endmembers is relatively small, a circumstance often observed in real
hyperspectral applications.
An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration
Authors: Andreas Mang, George Biros
Date: 2014-08-27 00:00:00
Tasks: Computational Efficiency, Constrained Diffeomorphic Image Registration, Image Registration

We propose numerical algorithms for solving large deformation diffeomorphic
image registration problems. We formulate the nonrigid image registration
problem as a problem of optimal control. This leads to an infinite-dimensional
partial differential equation (PDE) constrained optimization problem.
  The PDE constraint consists, in its simplest form, of a hyperbolic transport
equation for the evolution of the image intensity. The control variable is the
velocity field. Tikhonov regularization on the control ensures well-posedness.
We consider standard smoothness regularization based on $H^1$- or
$H^2$-seminorms. We augment this regularization scheme with a constraint on the
divergence of the velocity field rendering the deformation incompressible and
thus ensuring that the determinant of the deformation gradient is equal to one,
up to the numerical error.
  We use a Fourier pseudospectral discretization in space and a Chebyshev
pseudospectral discretization in time. We use a preconditioned, globalized,
matrix-free, inexact Newton-Krylov method for numerical optimization. A
parameter continuation is designed to estimate an optimal regularization
parameter. Regularity is ensured by controlling the geometric properties of the
deformation field. Overall, we arrive at a black-box solver. We study spectral
properties of the Hessian, grid convergence, numerical accuracy, computational
efficiency, and deformation regularity of our scheme. We compare the designed
Newton-Krylov methods with a globalized preconditioned gradient descent. We
study the influence of a varying number of unknowns in time.
  The reported results demonstrate excellent numerical accuracy, guaranteed
local deformation regularity, and computational efficiency with an optional
control on local mass conservation. The Newton-Krylov methods clearly
outperform the Picard method if high accuracy of the inversion is required.
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
Authors: Citlalli Gamez Serna, Ruven Pillay, Alain Tremeau
Date: 2015-05-07 00:00:00
Tasks: Translation

In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones.
LeoPARD --- A Generic Platform for the Implementation of Higher-Order Reasoners
Authors: Max Wisniewski, Alexander Steen, Christoph Benzmüller
Date: 2015-05-07 00:00:00

LeoPARD supports the implementation of knowledge representation and reasoning
tools for higher-order logic(s). It combines a sophisticated data structure
layer (polymorphically typed {\lambda}-calculus with nameless spine notation,
explicit substitutions, and perfect term sharing) with an ambitious multi-agent
blackboard architecture (supporting prover parallelism at the term, clause, and
search level). Further features of LeoPARD include a parser for all TPTP
dialects, a command line interpreter, and generic means for the integration of
external reasoners.
Bayesian Optimization for Synthetic Gene Design
Authors: Javier González, Joseph Longworth, David C. James, Neil D. Lawrence
Date: 2015-05-07 00:00:00
Tasks: Bayesian Optimization

We address the problem of synthetic gene design using Bayesian optimization.
The main issue when designing a gene is that the design space is defined in
terms of long strings of characters of different lengths, which renders the
optimization intractable. We propose a three-step approach to deal with this
issue. First, we use a Gaussian process model to emulate the behavior of the
cell. As inputs of the model, we use a set of biologically meaningful gene
features, which allows us to define optimal gene designs rules. Based on the
model outputs we define a multi-task acquisition function to optimize
simultaneously severals aspects of interest. Finally, we define an evaluation
function, which allow us to rank sets of candidate gene sequences that are
coherent with the optimal design strategy. We illustrate the performance of
this approach in a real gene design experiment with mammalian cells.
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach
Authors: Meryem Simsek, Mehdi Bennis, Ismail Güvenc
Date: 2015-05-07 00:00:00
Tasks: Fairness, Management, PICO, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of small cell deployments in heterogeneous network (HetNet)
environments is expected to be a key feature of 4G networks and beyond, and
essential for providing higher user throughput and cell-edge coverage. However,
due to different coverage sizes of macro and pico base stations (BSs), such a
paradigm shift introduces additional requirements and challenges in dense
networks. Among these challenges is the handover performance of user equipment
(UEs), which will be impacted especially when high velocity UEs traverse
picocells. In this paper, we propose a coordination-based and context-aware
mobility management (MM) procedure for small cell networks using tools from
reinforcement learning. Here, macro and pico BSs jointly learn their long-term
traffic loads and optimal cell range expansion, and schedule their UEs based on
their velocities and historical rates (exchanged among tiers). The proposed
approach is shown to not only outperform the classical MM in terms of UE
throughput, but also to enable better fairness. In average, a gain of up to
80\% is achieved for UE throughput, while the handover failure probability is
reduced up to a factor of three by the proposed learning based MM approaches.
Blind Compressive Sensing Framework for Collaborative Filtering
Authors: Anupriya Gogna, Angshul Majumdar
Date: 2015-05-07 00:00:00
Tasks: Collaborative Filtering, compressed sensing, Compressive Sensing

Existing works based on latent factor models have focused on representing the
rating matrix as a product of user and item latent factor matrices, both being
dense. Latent (factor) vectors define the degree to which a trait is possessed
by an item or the affinity of user towards that trait. A dense user matrix is a
reasonable assumption as each user will like/dislike a trait to certain extent.
However, any item will possess only a few of the attributes and never all.
Hence, the item matrix should ideally have a sparse structure rather than a
dense one as formulated in earlier works. Therefore we propose to factor the
ratings matrix into a dense user matrix and a sparse item matrix which leads us
to the Blind Compressed Sensing (BCS) framework. We derive an efficient
algorithm for solving the BCS problem based on Majorization Minimization (MM)
technique. Our proposed approach is able to achieve significantly higher
accuracy and shorter run times as compared to existing approaches.
Structure Formation in Large Theories
Authors: Serge Autexier, Dieter Hutter
Date: 2015-05-07 00:00:00
Tasks: Articles

Structuring theories is one of the main approaches to reduce the
combinatorial explosion associated with reasoning and exploring large theories.
In the past we developed the notion of development graphs as a means to
represent and maintain structured theories. In this paper we present a
methodology and a resulting implementation to reveal the hidden structure of
flat theories by transforming them into detailed development graphs. We review
our approach using plain TSTP-representations of MIZAR articles obtaining more
structured and also more concise theories.