pwc-archive/papers-with-abstracts huggingface dataset:
Mining Measured Information from Text
Authors: Arun S. Maiya, Dale Visser, Andrew Wan
Date: 2015-05-05 00:00:00
Tasks: valid

We present an approach to extract measured information from text (e.g., a
1370 degrees C melting point, a BMI greater than 29.9 kg/m^2 ). Such
extractions are critically important across a wide range of domains -
especially those involving search and exploration of scientific and technical
documents. We first propose a rule-based entity extractor to mine measured
quantities (i.e., a numeric value paired with a measurement unit), which
supports a vast and comprehensive set of both common and obscure measurement
units. Our method is highly robust and can correctly recover valid measured
quantities even when significant errors are introduced through the process of
converting document formats like PDF to plain text. Next, we describe an
approach to extracting the properties being measured (e.g., the property "pixel
pitch" in the phrase "a pixel pitch as high as 352 {\mu}m"). Finally, we
present MQSearch: the realization of a search engine with full support for
measured information.
A Spatiotemporal Context Definition for Service Adaptation Prediction in a Pervasive Computing Environment
Authors: Darine Ameyed, Moeiz Miraoui, Chakib Tadj
Date: 2015-05-05 00:00:00
Tasks: Prediction

Pervasive systems refers to context-aware systems that can sense their
context, and adapt their behavior accordingly to provide adaptable services.
Proactive adaptation of such systems allows changing the service and the
context based on prediction. However, the definition of the context is still
vague and not suitable to prediction. In this paper we discuss and classify
previous definitions of context. Then, we present a new definition which allows
pervasive systems to understand and predict their contexts. We analyze the
essential lines that fall within the context definition, and propose some
scenarios to make it clear our approach.
Fast Guided Filter
Authors: Kaiming He, Jian Sun
Date: 2015-05-05 00:00:00

The guided filter is a technique for edge-aware image filtering. Because of
its nice visual quality, fast speed, and ease of implementation, the guided
filter has witnessed various applications in real products, such as image
editing apps in phones and stereo reconstruction, and has been included in
official MATLAB and OpenCV. In this note, we remind that the guided filter can
be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In
a variety of applications, this leads to a speedup of >10x with almost no
visible degradation. We hope this acceleration will improve performance of
current applications and further popularize this filter. Code is released.
Stochastic Local Interaction (SLI) Model: Interfacing Machine Learning and Geostatistics
Authors: Dionissios T. Hristopulos
Date: 2015-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, valid

Machine learning and geostatistics are powerful mathematical frameworks for
modeling spatial data. Both approaches, however, suffer from poor scaling of
the required computational resources for large data applications. We present
the Stochastic Local Interaction (SLI) model, which employs a local
representation to improve computational efficiency. SLI combines geostatistics
and machine learning with ideas from statistical physics and computational
geometry. It is based on a joint probability density function defined by an
energy functional which involves local interactions implemented by means of
kernel functions with adaptive local kernel bandwidths. SLI is expressed in
terms of an explicit, typically sparse, precision (inverse covariance) matrix.
This representation leads to a semi-analytical expression for interpolation
(prediction), which is valid in any number of dimensions and avoids the
computationally costly covariance matrix inversion.
Kernel Machines for Current Status Data
Authors: Yael Travis-Lumer, Yair Goldberg
Date: 2015-05-05 00:00:00
Tasks: Survival Analysis

In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring. Specifically, in this paper we consider current status data, a type of data where all of the observations are censored. The format of the data is such that the failure time is restricted to knowledge of whether or not the failure time exceeds a random monitoring time. We propose a flexible kernel machine approach for estimation of the failure time expectation as a function of the covariates, with current status data. In order to obtain the kernel machine decision function, we minimize a regularized version of the empirical risk with respect to a new loss function. Using finite sample bounds and novel oracle inequalities, we prove that the obtained estimator converges to the true conditional expectation for a large family of probability measures. Finally, we present a simulation study and an analysis of real-world data that compares the performance of the proposed approach to existing methods. We show empirically that our approach is comparable to current state of the art, and in some cases is even better.
Incorporating Road Networks into Territory Design
Authors: Nitin Ahuja, Matthias Bender, Peter Sanders, Christian Schulz, Andreas Wagner
Date: 2015-04-29 00:00:00
Tasks: graph partitioning

Given a set of basic areas, the territory design problem asks to create a
predefined number of territories, each containing at least one basic area, such
that an objective function is optimized. Desired properties of territories
often include a reasonable balance, compact form, contiguity and small average
journey times which are usually encoded in the objective function or formulated
as constraints. We address the territory design problem by developing graph
theoretic models that also consider the underlying road network. The derived
graph models enable us to tackle the territory design problem by modifying
graph partitioning algorithms and mixed integer programming formulations so
that the objective of the planning problem is taken into account. We test and
compare the algorithms on several real world instances.
Autoencoding Time Series for Visualisation
Authors: Nikolaos Gianniotis, Dennis Kügler, Peter Tino, Kai Polsterer, Ranjeev Misra
Date: 2015-05-05 00:00:00
Tasks: Time Series, Time Series Analysis

We present an algorithm for the visualisation of time series. To that end we
employ echo state networks to convert time series into a suitable vector
representation which is capable of capturing the latent dynamics of the time
series. Subsequently, the obtained vector representations are put through an
autoencoder and the visualisation is constructed using the activations of the
bottleneck. The crux of the work lies with defining an objective function that
quantifies the reconstruction error of these representations in a principled
manner. We demonstrate the method on synthetic and real data.
Reinforced Decision Trees
Authors: Aurélia Léon, Ludovic Denoyer
Date: 2015-05-05 00:00:00
Tasks: Reinforcement Learning

In order to speed-up classification models when facing a large number of
categories, one usual approach consists in organizing the categories in a
particular structure, this structure being then used as a way to speed-up the
prediction computation. This is for example the case when using
error-correcting codes or even hierarchies of categories. But in the majority
of approaches, this structure is chosen \textit{by hand}, or during a
preliminary step, and not integrated in the learning process. We propose a new
model called Reinforced Decision Tree which simultaneously learns how to
organize categories in a tree structure and how to classify any input based on
this structure. This approach keeps the advantages of existing techniques (low
inference complexity) but allows one to build efficient classifiers in one
learning step. The learning algorithm is inspired by reinforcement learning and
policy-gradient techniques which allows us to integrate the two steps (building
the tree, and learning the classifier) in one single algorithm.
Domain-Size Pooling in Local Descriptors: DSP-SIFT
Authors: Jingming Dong, Stefano Soatto
Date: 2014-12-30 00:00:00
Proceeding: CVPR 2015 6

We introduce a simple modification of local image descriptors, such as SIFT,
based on pooling gradient orientations across different domain sizes, in
addition to spatial locations. The resulting descriptor, which we call
DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks,
including those based on convolutional neural networks, despite having the same
dimension of SIFT and requiring no training.
On the Feasibility of Distributed Kernel Regression for Big Data
Authors: Chen Xu, Yongquan Zhang, Runze Li
Date: 2015-05-05 00:00:00
Tasks: regression, valid

In modern scientific research, massive datasets with huge numbers of
observations are frequently encountered. To facilitate the computational
process, a divide-and-conquer scheme is often used for the analysis of big
data. In such a strategy, a full dataset is first split into several manageable
segments; the final output is then averaged from the individual outputs of the
segments. Despite its popularity in practice, it remains largely unknown that
whether such a distributive strategy provides valid theoretical inferences to
the original data. In this paper, we address this fundamental issue for the
distributed kernel regression (DKR), where the algorithmic feasibility is
measured by the generalization performance of the resulting estimator. To
justify DKR, a uniform convergence rate is needed for bounding the
generalization error over the individual outputs, which brings new and
challenging issues in the big data setup. Under mild conditions, we show that,
with a proper number of segments, DKR leads to an estimator that is
generalization consistent to the unknown regression function. The obtained
results justify the method of DKR and shed light on the feasibility of using
other distributed algorithms for processing big data. The promising preference
of the method is supported by both simulation and real data examples.
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition
Authors: Juan C. Moreno, V. B. Surya Prasath, D. Vorotnikov, H. Proenca, K. Palaniappan
Date: 2015-05-05 00:00:00
Tasks: Denoising

We consider an image decomposition model involving a variational
(minimization) problem and an evolutionary partial differential equation (PDE).
We utilize a linear inhomogenuous diffusion constrained and weighted total
variation (TV) scheme for image adaptive decomposition. An adaptive weight
along with TV regularization splits a given image into three components
representing the geometrical (cartoon), textural (small scale - microtextures),
and edges (big scale - macrotextures). We study the wellposedness of the
coupled variational-PDE scheme along with an efficient numerical scheme based
on Chambolle's dual minimization method. We provide extensive experimental
results in cartoon-texture-edges decomposition, and denoising as well compare
with other related variational, coupled anisotropic diffusion PDE based
methods.
A Feature-based Classification Technique for Answering Multi-choice World History Questions
Authors: Shuangyong Song, Yao Meng, Zhongguang Zheng, Jun Sun
Date: 2015-05-05 00:00:00
Tasks: General Classification, Sentence

Our FRDC_QA team participated in the QA-Lab English subtask of the NTCIR-11.
In this paper, we describe our system for solving real-world university
entrance exam questions, which are related to world history. Wikipedia is used
as the main external resource for our system. Since problems with choosing
right/wrong sentence from multiple sentence choices account for about
two-thirds of the total, we individually design a classification based model
for solving this type of questions. For other types of questions, we also
design some simple methods.
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
Authors: Babak Saleh, Ahmed Elgammal
Date: 2015-05-05 00:00:00
Tasks: General Classification, Metric Learning

In the past few years, the number of fine-art collections that are digitized
and publicly available has been growing rapidly. With the availability of such
large collections of digitized artworks comes the need to develop multimedia
systems to archive and retrieve this pool of data. Measuring the visual
similarity between artistic items is an essential step for such multimedia
systems, which can benefit more high-level multimedia tasks. In order to model
this similarity between paintings, we should extract the appropriate visual
features for paintings and find out the best approach to learn the similarity
metric based on these features. We investigate a comprehensive list of visual
features and metric learning approaches to learn an optimized similarity
measure between paintings. We develop a machine that is able to make
aesthetic-related semantic-level judgments, such as predicting a painting's
style, genre, and artist, as well as providing similarity measures optimized
based on the knowledge available in the domain of art historical
interpretation. Our experiments show the value of using this similarity measure
for the aforementioned prediction tasks.
Locally Non-rigid Registration for Mobile HDR Photography
Authors: Orazio Gallo, Alejandro Troccoli, Jun Hu, Kari Pulli, Jan Kautz
Date: 2015-04-07 00:00:00
Tasks: Image Registration

Image registration for stack-based HDR photography is challenging. If not
properly accounted for, camera motion and scene changes result in artifacts in
the composite image. Unfortunately, existing methods to address this problem
are either accurate, but too slow for mobile devices, or fast, but prone to
failing. We propose a method that fills this void: our approach is extremely
fast---under 700ms on a commercial tablet for a pair of 5MP images---and
prevents the artifacts that arise from insufficient registration quality.
A novel plasticity rule can explain the development of sensorimotor intelligence
Authors: Ralf Der, Georg Martius
Date: 2015-05-04 00:00:00
Tasks: Self-Learning

Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.
Pattern Recognition in Narrative: Tracking Emotional Expression in Context
Authors: Fionn Murtagh, Adam Ganz
Date: 2014-05-14 00:00:00

Using geometric data analysis, our objective is the analysis of narrative,
with narrative of emotion being the focus in this work. The following two
principles for analysis of emotion inform our work. Firstly, emotion is
revealed not as a quality in its own right but rather through interaction. We
study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the
3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame
Bovary}. Secondly, emotion, that is expression of states of mind of subjects,
is formed and evolves within the narrative that expresses external events and
(personal, social, physical) context. In addition to the analysis methodology
with key aspects that are innovative, the input data used is crucial. We use,
firstly, dialogue, and secondly, broad and general description that
incorporates dialogue. In a follow-on study, we apply our unsupervised
narrative mapping to data streams with very low emotional expression. We map
the narrative of Twitter streams. Thus we demonstrate map analysis of general
narratives.
Self-Expressive Decompositions for Matrix Approximation and Clustering
Authors: Eva L. Dyer, Tom A. Goldstein, Raajen Patel, Konrad P. Kording, Richard G. Baraniuk
Date: 2015-05-04 00:00:00
Tasks: Clustering, Denoising, Dimensionality Reduction

Data-aware methods for dimensionality reduction and matrix decomposition aim
to find low-dimensional structure in a collection of data. Classical approaches
discover such structure by learning a basis that can efficiently express the
collection. Recently, "self expression", the idea of using a small subset of
data vectors to represent the full collection, has been developed as an
alternative to learning. Here, we introduce a scalable method for computing
sparse SElf-Expressive Decompositions (SEED). SEED is a greedy method that
constructs a basis by sequentially selecting incoherent vectors from the
dataset. After forming a basis from a subset of vectors in the dataset, SEED
then computes a sparse representation of the dataset with respect to this
basis. We develop sufficient conditions under which SEED exactly represents low
rank matrices and vectors sampled from a unions of independent subspaces. We
show how SEED can be used in applications ranging from matrix approximation and
denoising to clustering, and apply it to numerous real-world datasets. Our
results demonstrate that SEED is an attractive low-complexity alternative to
other sparse matrix factorization approaches such as sparse PCA and
self-expressive methods for clustering.
Towards the Ontology Web Search Engine
Authors: Olegs Verhodubs
Date: 2015-05-04 00:00:00

The project of the Ontology Web Search Engine is presented in this paper. The
main purpose of this paper is to develop such a project that can be easily
implemented. Ontology Web Search Engine is software to look for and index
ontologies in the Web. OWL (Web Ontology Languages) ontologies are meant, and
they are necessary for the functioning of the SWES (Semantic Web Expert
System). SWES is an expert system that will use found ontologies from the Web,
generating rules from them, and will supplement its knowledge base with these
generated rules. It is expected that the SWES will serve as a universal expert
system for the average user.
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
Authors: Mrinal Haloi, Samarendra Dandapat, Rohit Sinha
Date: 2015-05-04 00:00:00
Tasks: General Classification, Prediction, Sensitivity, severity prediction

In the context of Computer Aided Diagnosis system for diabetic retinopathy,
we present a novel method for detection of exudates and their classification
for disease severity prediction. The method is based on Gaussian scale space
based interest map and mathematical morphology. It makes use of support vector
machine for classification and location information of the optic disc and the
macula region for severity prediction. It can efficiently handle luminance
variation and it is suitable for varied sized exudates. The method has been
probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate
detection the proposed method achieved a sensitivity of 96.54% and prediction
of 98.35% in DIARETDB1V2 database.
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
Authors: Minsu Cho, Suha Kwak, Cordelia Schmid, Jean Ponce
Date: 2015-01-25 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Discovery

This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.
Optimizations for Decision Making and Planning in Description Logic Dynamic Knowledge Bases
Authors: Michele Stawowy
Date: 2015-02-16 00:00:00
Tasks: Decision Making

Artifact-centric models for business processes recently raised a lot of
attention, as they manage to combine structural (i.e. data related) with
dynamical (i.e. process related) aspects in a seamless way. Many frameworks
developed under this approach, although, are not built explicitly for planning,
one of the most prominent operations related to business processes. In this
paper, we try to overcome this by proposing a framework named Dynamic Knowledge
Bases, aimed at describing rich business domains through Description
Logic-based ontologies, and where a set of actions allows the system to evolve
by modifying such ontologies. This framework, by offering action rewriting and
knowledge partialization, represents a viable and formal environment to develop
decision making and planning techniques for DL-based artifact-centric business
domains.
Penalized versus constrained generalized eigenvalue problems
Authors: Irina Gaynanova, James Booth, Martin T. Wells
Date: 2014-10-22 00:00:00
Tasks: Variable Selection

We investigate the difference between using an $\ell_1$ penalty versus an
$\ell_1$ constraint in generalized eigenvalue problems, such as principal
component analysis and discriminant analysis. Our main finding is that an
$\ell_1$ penalty may fail to provide very sparse solutions; a severe
disadvantage for variable selection that can be remedied by using an $\ell_1$
constraint. Our claims are supported both by empirical evidence and theoretical
analysis. Finally, we illustrate the advantages of an $\ell_1$ constraint in
the context of discriminant analysis and principal component analysis.