pwc-archive/papers-with-abstracts huggingface dataset:
Structure-preserving color transformations using Laplacian commutativity
Authors: Davide Eynard, Artiom Kovnatsky, Michael M. Bronstein
Date: 2013-11-01 00:00:00

Mappings between color spaces are ubiquitous in image processing problems
such as gamut mapping, decolorization, and image optimization for color-blind
people. Simple color transformations often result in information loss and
ambiguities (for example, when mapping from RGB to grayscale), and one wishes
to find an image-specific transformation that would preserve as much as
possible the structure of the original image in the target color space. In this
paper, we propose Laplacian colormaps, a generic framework for
structure-preserving color transformations between images. We use the image
Laplacian to capture the structural information, and show that if the color
transformation between two images preserves the structure, the respective
Laplacians have similar eigenvectors, or in other words, are approximately
jointly diagonalizable. Employing the relation between joint diagonalizability
and commutativity of matrices, we use Laplacians commutativity as a criterion
of color mapping quality and minimize it w.r.t. the parameters of a color
transformation to achieve optimal structure preservation. We show numerous
applications of our approach, including color-to-gray conversion, gamut
mapping, multispectral image fusion, and image optimization for color deficient
viewers.
Bayesian inference as iterated random functions with applications to sequential inference in graphical models
Authors: Arash A. Amini, XuanLong Nguyen
Date: 2013-11-01 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Bayesian Inference, Change Point Detection

We propose a general formalism of iterated random functions with semigroup
property, under which exact and approximate Bayesian posterior updates can be
viewed as specific instances. A convergence theory for iterated random
functions is presented. As an application of the general theory we analyze
convergence behaviors of exact and approximate message-passing algorithms that
arise in a sequential change point detection problem formulated via a latent
variable directed graphical model. The sequential inference algorithm and its
supporting theory are illustrated by simulated examples.
Parameterless Optimal Approximate Message Passing
Authors: Ali Mousavi, Arian Maleki, Richard G. Baraniuk
Date: 2013-10-31 00:00:00
Tasks: Compressive Sensing

Iterative thresholding algorithms are well-suited for high-dimensional
problems in sparse recovery and compressive sensing. The performance of this
class of algorithms depends heavily on the tuning of certain threshold
parameters. In particular, both the final reconstruction error and the
convergence rate of the algorithm crucially rely on how the threshold parameter
is set at each step of the algorithm. In this paper, we propose a
parameter-free approximate message passing (AMP) algorithm that sets the
threshold parameter at each iteration in a fully automatic way without either
having an information about the signal to be reconstructed or needing any
tuning from the user. We show that the proposed method attains both the minimum
reconstruction error and the highest convergence rate. Our method is based on
applying the Stein unbiased risk estimate (SURE) along with a modified gradient
descent to find the optimal threshold in each iteration. Motivated by the
connections between AMP and LASSO, it could be employed to find the solution of
the LASSO for the optimal regularization parameter. To the best of our
knowledge, this is the first work concerning parameter tuning that obtains the
fastest convergence rate with theoretical guarantees.
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary
Authors: Jie Chen, Wei Gao, Cédric Richard, Jose-Carlos M. Bermudez
Date: 2013-10-31 00:00:00

The kernel least-mean-square (KLMS) algorithm is an appealing tool for online
identification of nonlinear systems due to its simplicity and robustness. In
addition to choosing a reproducing kernel and setting filter parameters,
designing a KLMS adaptive filter requires to select a so-called dictionary in
order to get a finite-order model. This dictionary has a significant impact on
performance, and requires careful consideration. Theoretical analysis of KLMS
as a function of dictionary setting has rarely, if ever, been addressed in the
literature. In an analysis previously published by the authors, the dictionary
elements were assumed to be governed by the same probability density function
of the input data. In this paper, we modify this study by considering the
dictionary as part of the filter parameters to be set. This theoretical
analysis paves the way for future investigations on KLMS dictionary design.
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization
Authors: Jie Chen, Cédric Richard, Alfred O. Hero III
Date: 2013-10-31 00:00:00
Tasks: Hyperspectral Unmixing

Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a variational approach to incorporating spatial correlation into a
nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing
kernel Hilbert spaces, associated with an $\ell_1$ local variation norm as the
spatial regularizer, is derived. Experimental results, with both synthetic and
real data, illustrate the effectiveness of the proposed scheme.
A Hybrid Local Search for Simplified Protein Structure Prediction
Authors: Swakkhar Shatabda, M. A. Hakim Newton, Duc Nghia Pham, Abdul Sattar
Date: 2013-10-31 00:00:00
Tasks: Protein Structure Prediction

Protein structure prediction based on Hydrophobic-Polar energy model
essentially becomes searching for a conformation having a compact hydrophobic
core at the center. The hydrophobic core minimizes the interaction energy
between the amino acids of the given protein. Local search algorithms can
quickly find very good conformations by moving repeatedly from the current
solution to its "best" neighbor. However, once such a compact hydrophobic core
is found, the search stagnates and spends enormous effort in quest of an
alternative core. In this paper, we attempt to restructure segments of a
conformation with such compact core. We select one large segment or a number of
small segments and apply exhaustive local search. We also apply a mix of
heuristics so that one heuristic can help escape local minima of another. We
evaluated our algorithm by using Face Centered Cubic (FCC) Lattice on a set of
standard benchmark proteins and obtain significantly better results than that
of the state-of-the-art methods.
Spatial statistics, image analysis and percolation theory
Authors: Mikhail Langovoy, Michael Habeck, Bernhard Schölkopf
Date: 2013-10-31 00:00:00
Tasks: object-detection, Object Detection, Two-sample testing

We develop a novel method for detection of signals and reconstruction of
images in the presence of random noise. The method uses results from
percolation theory. We specifically address the problem of detection of
multiple objects of unknown shapes in the case of nonparametric noise. The
noise density is unknown and can be heavy-tailed. The objects of interest have
unknown varying intensities. No boundary shape constraints are imposed on the
objects, only a set of weak bulk conditions is required. We view the object
detection problem as a multiple hypothesis testing for discrete statistical
inverse problems. We present an algorithm that allows to detect greyscale
objects of various shapes in noisy images. We prove results on consistency and
algorithmic complexity of our procedures. Applications to cryo-electron
microscopy are presented.
A dependent partition-valued process for multitask clustering and time evolving network modelling
Authors: Konstantina Palla, David A. Knowles, Zoubin Ghahramani
Date: 2013-03-13 00:00:00
Tasks: Clustering, Gaussian Processes, Time Series, Time Series Analysis

The fundamental aim of clustering algorithms is to partition data points. We
consider tasks where the discovered partition is allowed to vary with some
covariate such as space or time. One approach would be to use
fragmentation-coagulation processes, but these, being Markov processes, are
restricted to linear or tree structured covariate spaces. We define a
partition-valued process on an arbitrary covariate space using Gaussian
processes. We use the process to construct a multitask clustering model which
partitions datapoints in a similar way across multiple data sources, and a time
series model of network data which allows cluster assignments to vary over
time. We describe sampling algorithms for inference and apply our method to
defining cancer subtypes based on different types of cellular characteristics,
finding regulatory modules from gene expression data from multiple human
populations, and discovering time varying community structure in a social
network.
Reinforcement Learning Framework for Opportunistic Routing in WSNs
Authors: G. Srinivas Rao, A. V. Ramana
Date: 2013-10-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Routing packets opportunistically is an essential part of multihop ad hoc
wireless sensor networks. The existing routing techniques are not adaptive
opportunistic. In this paper we have proposed an adaptive opportunistic routing
scheme that routes packets opportunistically in order to ensure that packet
loss is avoided. Learning and routing are combined in the framework that
explores the optimal routing possibilities. In this paper we implemented this
Reinforced learning framework using a customer simulator. The experimental
results revealed that the scheme is able to exploit the opportunistic to
optimize routing of packets even though the network structure is unknown.
An Improved K-means Clustering Based Approach to Detect a DNA Structure in H&E Image of Mouse Tissue Reacted with CD4-Green Antigen
Authors: B. U. V Prashanth, P Narahari Sastry, V Rajesh
Date: 2013-10-12 00:00:00
Tasks: Clustering

In this manuscript we present the technique to detect and analyze the DNA
rich structure in Haemotoxylin & Eosin (H&E) image of a tissue treated with
anti CD4 green antigen. The detection of DNA rich structure can be considered
as a detection of blue nuclei present through the biomedical signal/image
processing technique performed on the image of the tissue obtained by the
Scanning Electron Microscope(SEM). Earlier the tissue treated with the anti CD4
green antigen, is stained with the H&E staining solution.
Novel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data
Authors: Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, Misha Kilmer
Date: 2013-07-02 00:00:00
Tasks: Data Compression, Tensor Decomposition

In this paper we propose novel methods for compression and recovery of
multilinear data under limited sampling. We exploit the recently proposed
tensor- Singular Value Decomposition (t-SVD)[1], which is a group theoretic
framework for tensor decomposition. In contrast to popular existing tensor
decomposition techniques such as higher-order SVD (HOSVD), t-SVD has optimality
properties similar to the truncated SVD for matrices. Based on t-SVD, we first
construct novel tensor-rank like measures to characterize informational and
structural complexity of multilinear data. Following that we outline a
complexity penalized algorithm for tensor completion from missing entries. As
an application, 3-D and 4-D (color) video data compression and recovery are
considered. We show that videos with linear camera motion can be represented
more efficiently using t-SVD compared to traditional approaches based on
vectorizing or flattening of the tensors. Application of the proposed tensor
completion algorithm for video recovery from missing entries is shown to yield
a superior performance over existing methods. In conclusion we point out
several research directions and implications to online prediction of
multilinear data.
Safe and Efficient Screening For Sparse Support Vector Machine
Authors: Zheng Zhao, Jun Liu
Date: 2013-10-30 00:00:00
Tasks: Sparse Learning

Screening is an effective technique for speeding up the training process of a
sparse learning model by removing the features that are guaranteed to be
inactive the process. In this paper, we present a efficient screening technique
for sparse support vector machine based on variational inequality. The
technique is both efficient and safe.
Distributed k-Means and k-Median Clustering on General Topologies
Authors: Maria Florina Balcan, Steven Ehrlich, YIngyu Liang
Date: 2013-06-03 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering

This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the problem of finding a clustering with low cost to the problem of finding a coreset of small size. We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies. Experimental results on large scale data sets show that this approach outperforms other coreset-based distributed clustering algorithms.
Para-active learning
Authors: Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford
Date: 2013-10-30 00:00:00
Tasks: Active Learning

Training examples are not all equally informative. Active learning strategies
leverage this observation in order to massively reduce the number of examples
that need to be labeled. We leverage the same observation to build a generic
strategy for parallelizing learning algorithms. This strategy is effective
because the search for informative examples is highly parallelizable and
because we show that its performance does not deteriorate when the sifting
process relies on a slightly outdated model. Parallel active learning is
particularly attractive to train nonlinear models with non-linear
representations because there are few practical parallel learning algorithms
for such models. We report preliminary experiments using both kernel SVMs and
SGD-trained neural networks.
Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping
Authors: Jakub M. Tomczak
Date: 2013-08-28 00:00:00
Tasks: General Classification

In this paper, we apply Classification Restricted Boltzmann Machine
(ClassRBM) to the problem of predicting breast cancer recurrence. According to
the Polish National Cancer Registry, in 2010 only, the breast cancer caused
almost 25% of all diagnosed cases of cancer in Poland. We propose how to use
ClassRBM for predicting breast cancer return and discovering relevant inputs
(symptoms) in illness reappearance. Next, we outline a general probabilistic
framework for learning Boltzmann machines with masks, which we refer to as
Dropping. The fashion of generating masks leads to different learning methods,
i.e., DropOut, DropConnect. We propose a new method called DropPart which is a
generalization of DropConnect. In DropPart the Beta distribution instead of
Bernoulli distribution in DropConnect is used. At the end, we carry out an
experiment using real-life dataset consisting of 949 cases, provided by the
Institute of Oncology Ljubljana.
Compressive Optical Deflectometric Tomography: A Constrained Total-Variation Minimization Approach
Authors: Adriana Gonzalez, Laurent Jacques, Christophe De Vleeschouwer, Philippe Antoine
Date: 2012-09-04 00:00:00

Optical Deflectometric Tomography (ODT) provides an accurate characterization
of transparent materials whose complex surfaces present a real challenge for
manufacture and control. In ODT, the refractive index map (RIM) of a
transparent object is reconstructed by measuring light deflection under
multiple orientations. We show that this imaging modality can be made
"compressive", i.e., a correct RIM reconstruction is achievable with far less
observations than required by traditional Filtered Back Projection (FBP)
methods. Assuming a cartoon-shape RIM model, this reconstruction is driven by
minimizing the map Total-Variation under a fidelity constraint with the
available observations. Moreover, two other realistic assumptions are added to
improve the stability of our approach: the map positivity and a frontier
condition. Numerically, our method relies on an accurate ODT sensing model and
on a primal-dual minimization scheme, including easily the sensing operator and
the proposed RIM constraints. We conclude this paper by demonstrating the power
of our method on synthetic and experimental data under various compressive
scenarios. In particular, the compressiveness of the stabilized ODT problem is
demonstrated by observing a typical gain of 20 dB compared to FBP at only 5% of
360 incident light angles for moderately noisy sensing.
Artificial Intelligence in Humans
Authors: Michael Swan Laufer
Date: 2013-10-30 00:00:00

In this paper, I put forward that in many instances, thinking mechanisms are
equivalent to artificial intelligence modules programmed into the human mind.
On the Tractability of Minimal Model Computation for Some CNF Theories
Authors: Fabrizio Angiulli, Rachel Ben-Eliyahu-Zohary, Fabio Fassetti, Luigi Palopoli
Date: 2013-10-30 00:00:00

Designing algorithms capable of efficiently constructing minimal models of
CNFs is an important task in AI. This paper provides new results along this
research line and presents new algorithms for performing minimal model finding
and checking over positive propositional CNFs and model minimization over
propositional CNFs. An algorithmic schema, called the Generalized Elimination
Algorithm (GEA) is presented, that computes a minimal model of any positive
CNF. The schema generalizes the Elimination Algorithm (EA) [BP97], which
computes a minimal model of positive head-cycle-free (HCF) CNF theories. While
the EA always runs in polynomial time in the size of the input HCF CNF, the
complexity of the GEA depends on the complexity of the specific eliminating
operator invoked therein, which may in general turn out to be exponential.
Therefore, a specific eliminating operator is defined by which the GEA
computes, in polynomial time, a minimal model for a class of CNF that strictly
includes head-elementary-set-free (HEF) CNF theories [GLL06], which form, in
their turn, a strict superset of HCF theories. Furthermore, in order to deal
with the high complexity associated with recognizing HEF theories, an
"incomplete" variant of the GEA (called IGEA) is proposed: the resulting
schema, once instantiated with an appropriate elimination operator, always
constructs a model of the input CNF, which is guaranteed to be minimal if the
input theory is HEF. In the light of the above results, the main contribution
of this work is the enlargement of the tractability frontier for the minimal
model finding and checking and the model minimization problems.
Outlier detection in default logics: the tractability/intractability frontier
Authors: F. Angiulli, R. Ben-Eliyahu-Zohary, L. Palopoli
Date: 2011-09-21 00:00:00
Tasks: LEMMA, Outlier Detection

In default theories, outliers denote sets of literals featuring unexpected
properties. In previous papers, we have defined outliers in default logics and
investigated their formal properties. Specifically, we have looked into the
computational complexity of outlier detection problems and proved that while
they are generally intractable, interesting tractable cases can be singled out.
Following those results, we study here the tractability frontier in outlier
detection problems, by analyzing it with respect to (i) the considered outlier
detection problem, (ii) the reference default logic fragment, and (iii) the
adopted notion of outlier. As for point (i), we shall consider three problems
of increasing complexity, called Outlier-Witness Recognition, Outlier
Recognition and Outlier Existence, respectively. As for point (ii), as we look
for conditions under which outlier detection can be done efficiently, attention
will be limited to subsets of Disjunction-free propositional default theories.
As for point (iii), we shall refer to both the notion of outlier of [ABP08] and
a new and more restrictive one, called strong outlier. After complexity
results, we present a polynomial time algorithm for enumerating all strong
outliers of bounded size in an quasi-acyclic normal unary default theory. Some
of our tractability results rely on the Incremental Lemma that provides
conditions for a deafult logic fragment to have a monotonic behavior. Finally,
in order to show that the simple fragments of DL we deal with are still rich
enough to solve interesting problems and, therefore, the tractability results
that we prove are interesting not only on the mere theoretical side, insights
into the expressive capabilities of these fragments are provided, by showing
that normal unary theories express all NL queries, hereby indirectly answering
a question raised by Kautz and Selman.
Tracking Deformable Parts via Dynamic Conditional Random Fields
Authors: Suofei Zhang, Zhixin Sun, Xu Cheng, Zhenyang Wu
Date: 2013-10-30 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking, Occlusion Handling

Despite the success of many advanced tracking methods in this area, tracking
targets with drastic variation of appearance such as deformation, view change
and partial occlusion in video sequences is still a challenge in practical
applications. In this letter, we take these serious tracking problems into
account simultaneously, proposing a dynamic graph based model to track object
and its deformable parts at multiple resolutions. The method introduces well
learned structural object detection models into object tracking applications as
prior knowledge to deal with deformation and view change. Meanwhile, it
explicitly formulates partial occlusion by integrating spatial potentials and
temporal potentials with an unparameterized occlusion handling mechanism in the
dynamic conditional random field framework. Empirical results demonstrate that
the method outperforms state-of-the-art trackers on different challenging video
sequences.
Description and Evaluation of Semantic Similarity Measures Approaches
Authors: Thabet Slimani
Date: 2013-10-30 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

In recent years, semantic similarity measure has a great interest in Semantic
Web and Natural Language Processing (NLP). Several similarity measures have
been developed, being given the existence of a structured knowledge
representation offered by ontologies and corpus which enable semantic
interpretation of terms. Semantic similarity measures compute the similarity
between concepts/terms included in knowledge sources in order to perform
estimations. This paper discusses the existing semantic similarity methods
based on structure, information content and feature approaches. Additionally,
we present a critical evaluation of several categories of semantic similarity
approaches based on two standard benchmarks. The aim of this paper is to give
an efficient evaluation of all these measures which help researcher and
practitioners to select the measure that best fit for their requirements.