pwc-archive/papers-with-abstracts huggingface dataset:
Release Early, Release Often: Predicting Change in Versioned Knowledge Organization Systems on the Web
Authors: Albert Meroño-Peñuela, Christophe Guéret, Stefan Schlobach
Date: 2015-05-12 00:00:00
Tasks: Philosophy

The Semantic Web is built on top of Knowledge Organization Systems (KOS)
(vocabularies, ontologies, concept schemes) that provide a structured,
interoperable and distributed access to Linked Data on the Web. The maintenance
of these KOS over time has produced a number of KOS version chains: subsequent
unique version identifiers to unique states of a KOS. However, the release of
new KOS versions pose challenges to both KOS publishers and users. For
publishers, updating a KOS is a knowledge intensive task that requires a lot of
manual effort, often implying deep deliberation on the set of changes to
introduce. For users that link their datasets to these KOS, a new version
compromises the validity of their links, often creating ramifications. In this
paper we describe a method to automatically detect which parts of a Web KOS are
likely to change in a next version, using supervised learning on past versions
in the KOS version chain. We use a set of ontology change features to model and
predict change in arbitrary Web KOS. We apply our method on 139 varied datasets
systematically retrieved from the Semantic Web, obtaining robust results at
correctly predicting change. To illustrate the accuracy, genericity and domain
independence of the method, we study the relationship between its effectiveness
and several characterizations of the evaluated datasets, finding that
predictors like the number of versions in a chain and their release frequency
have a fundamental impact in predictability of change in Web KOS. Consequently,
we argue for adopting a release early, release often philosophy in Web KOS
development cycles.
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
Authors: Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers
Date: 2015-05-12 00:00:00

Automated computer-aided detection (CADe) in medical imaging has been an
important tool in clinical practice and research. State-of-the-art methods
often show high sensitivities but at the cost of high false-positives (FP) per
patient rates. We design a two-tiered coarse-to-fine cascade framework that
first operates a candidate generation system at sensitivities of $\sim$100% but
at high FP levels. By leveraging existing CAD systems, coordinates of regions
or volumes of interest (ROI or VOI) for lesion candidates are generated in this
step and function as input for a second tier, which is our focus in this study.
In this second stage, we generate $N$ 2D (two-dimensional) or 2.5D views via
sampling through scale transformations, random translations and rotations with
respect to each ROI's centroid coordinates. These random views are used to
train deep convolutional neural network (ConvNet) classifiers. In testing, the
trained ConvNets are employed to assign class (e.g., lesion, pathology)
probabilities for a new set of $N$ random views that are then averaged at each
ROI to compute a final per-candidate classification probability. This second
tier behaves as a highly selective process to reject difficult false positives
while preserving high sensitivities. The methods are evaluated on three
different data sets with different numbers of patients: 59 patients for
sclerotic metastases detection, 176 patients for lymph node detection, and
1,186 patients for colonic polyp detection. Experimental results show the
ability of ConvNets to generalize well to different medical imaging CADe
applications and scale elegantly to various data sets. Our proposed methods
improve CADe performance markedly in all cases. CADe sensitivities improved
from 57% to 70%, from 43% to 77% and from 58% to 75% at 3 FPs per patient for
sclerotic metastases, lymph nodes and colonic polyps, respectively.
Learning the Structure for Structured Sparsity
Authors: Nino Shervashidze, Francis Bach
Date: 2015-03-10 00:00:00
Tasks: Denoising

Structured sparsity has recently emerged in statistics, machine learning and
signal processing as a promising paradigm for learning in high-dimensional
settings. All existing methods for learning under the assumption of structured
sparsity rely on prior knowledge on how to weight (or how to penalize)
individual subsets of variables during the subset selection process, which is
not available in general. Inferring group weights from data is a key open
research problem in structured sparsity.In this paper, we propose a Bayesian
approach to the problem of group weight learning. We model the group weights as
hyperparameters of heavy-tailed priors on groups of variables and derive an
approximate inference scheme to infer these hyperparameters. We empirically
show that we are able to recover the model hyperparameters when the data are
generated from the model, and we demonstrate the utility of learning weights in
synthetic and real denoising problems.
How Many Communities Are There?
Authors: Diego Franco Saldana, Yi Yu, Yang Feng
Date: 2014-12-04 00:00:00
Tasks: Clustering, Community Detection, Model Selection

Stochastic blockmodels and variants thereof are among the most widely used
approaches to community detection for social networks and relational data. A
stochastic blockmodel partitions the nodes of a network into disjoint sets,
called communities. The approach is inherently related to clustering with
mixture models; and raises a similar model selection problem for the number of
communities. The Bayesian information criterion (BIC) is a popular solution,
however, for stochastic blockmodels, the conditional independence assumption
given the communities of the endpoints among different edges is usually
violated in practice. In this regard, we propose composite likelihood BIC
(CL-BIC) to select the number of communities, and we show it is robust against
possible misspecifications in the underlying stochastic blockmodel assumptions.
We derive the requisite methodology and illustrate the approach using both
simulated and real data. Supplementary materials containing the relevant
computer code are available online.
Direct high-order edge-preserving regularization for tomographic image reconstruction
Authors: Daniil Kazantsev, Evgueni Ovtchinnikov, William R. B. Lionheart, Philip J. Withers, Peter D. Lee
Date: 2015-09-15 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction, Vocal Bursts Intensity Prediction

In this paper we present a new two-level iterative algorithm for tomographic
image reconstruction. The algorithm uses a regularization technique, which we
call edge-preserving Laplacian, that preserves sharp edges between objects
while damping spurious oscillations in the areas where the reconstructed image
is smooth. Our numerical simulations demonstrate that the proposed method
outperforms total variation (TV) regularization and it is competitive with the
combined TV-L2 penalty. Obtained reconstructed images show increased
signal-to-noise ratio and visually appealing structural features. Computer
implementation and parameter control of the proposed technique is
straightforward, which increases the feasibility of it across many tomographic
applications. In this paper, we applied our method to the under-sampled
computed tomography (CT) projection data and also considered a case of
reconstruction in emission tomography The MATLAB code is provided to support
obtained results.
Self-Configuring and Evolving Fuzzy Image Thresholding
Authors: A. Othman, H. R. Tizhoosh, F. Khalvati
Date: 2015-09-15 00:00:00
Tasks: feature selection, Image Segmentation, Segmentation, Semantic Segmentation

Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).
Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm
Authors: N. N. Astakhova, L. A. Demidova, E. V. Nikulchev
Date: 2015-09-15 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Forecasting

The paper is focused on the forecasting method for time series groups with
the use of algorithms for cluster analysis. $K$-means algorithm is suggested to
be a basic one for clustering. The coordinates of the centers of clusters have
been put in correspondence with summarizing time series data the centroids of
the clusters. A description of time series, the centroids of the clusters, is
implemented with the use of forecasting models. They are based on strict binary
trees and a modified clonal selection algorithm. With the help of such
forecasting models, the possibility of forming analytic dependences is shown.
It is suggested to use a common forecasting model, which is constructed for
time series the centroid of the cluster, in forecasting the private
(individual) time series in the cluster. The promising application of the
suggested method for grouped time series forecasting is demonstrated.
Dynamic Poisson Factorization
Authors: Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
Date: 2015-09-15 00:00:00
Tasks: Recommendation Systems, Variational Inference

Models for recommender systems use latent factors to explain the preferences
and behaviors of users with respect to a set of items (e.g., movies, books,
academic papers). Typically, the latent factors are assumed to be static and,
given these factors, the observed preferences and behaviors of users are
assumed to be generated without order. These assumptions limit the explorative
and predictive capabilities of such models, since users' interests and item
popularity may evolve over time. To address this, we propose dPF, a dynamic
matrix factorization model based on the recent Poisson factorization model for
recommendations. dPF models the time evolving latent factors with a Kalman
filter and the actions with Poisson distributions. We derive a scalable
variational inference algorithm to infer the latent factors. Finally, we
demonstrate dPF on 10 years of user click data from arXiv.org, one of the
largest repository of scientific papers and a formidable source of information
about the behavior of scientists. Empirically we show performance improvement
over both static and, more recently proposed, dynamic recommendation models. We
also provide a thorough exploration of the inferred posteriors over the latent
variables.
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Authors: Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati
Date: 2015-09-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Medical Diagnosis, Medical Image Classification, Retrieval

Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.
Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis
Authors: Wei Zhang, Jae-Woong Chang, Lilong Lin, Kay Minn, Baolin Wu, Jeremy Chien, Jeongsik Yong, Hui Zheng, Rui Kuang
Date: 2014-03-20 00:00:00

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript
quantification of gene isoforms. Since RNA-Seq data alone is often not
sufficient to accurately identify the read origins from the isoforms for
quantification, we propose to explore protein domain-domain interactions as
prior knowledge for integrative analysis with RNA-seq data. We introduce a
Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to
integrate protein domain-domain interaction network with short read alignments
for transcript abundance estimation. Based on our observation that the
abundances of the neighboring isoforms by domain-domain interactions in the
network are positively correlated, Net-RSTQ models the expression of the
neighboring transcripts as Dirichlet priors on the likelihood of the observed
read alignments against the transcripts in one gene. The transcript abundances
of all the genes are then jointly estimated with alternating optimization of
multiple EM problems. In simulation Net-RSTQ effectively improved isoform
transcript quantifications when isoform co-expressions correlate with their
interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an
ovarian cancer cell line, and a breast cancer cell line also showed that
Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In
the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the
transcript abundances estimated by Net-RSTQ are more informative for patient
sample classification of ovarian cancer, breast cancer and lung cancer. All
experimental results collectively support that Net-RSTQ is a promising approach
for isoform quantification.
New results on inconsistency indices and their relationship with the quality of priority vector estimation
Authors: Andrzej Z. Grzybowski
Date: 2015-05-25 00:00:00
Tasks: Decision Making

The article is devoted to the problem of inconsistency in the pairwise
comparisons based prioritization methodology. The issue of "inconsistency" in
this context has gained much attention in recent years. The literature provides
us with a number of different "inconsistency" indices suggested for measuring
the inconsistency of the pairwise comparison matrix (PCM). The latter is
understood as a deviation of the PCM from the "consistent case" - a notion that
is formally well-defined in this theory. However the usage of the indices is
justified only by some heuristics. It is still unclear what they really
"measure". What is even more important and still not known is the relationship
between their values and the "consistency" of the decision maker's judgments on
one hand, and the prioritization results upon the other. We provide examples
showing that it is necessary to distinguish between these three following
tasks: the "measuring" of the "PCM inconsistency" and the PCM-based "measuring"
of the consistency of decision maker's judgments and, finally, the "measuring"
of the usefulness of the PCM as a source of information for estimation of the
priority vector (PV). Next we focus on the third task, which seems to be the
most important one in Multi-Criteria Decision Making. With the help of Monte
Carlo experiments, we study the performance of various inconsistency indices as
indicators of the final PV estimation quality. The presented results allow a
deeper understanding of the information contained in these indices and help in
choosing a proper one in a given situation. They also enable us to develop a
new inconsistency characteristic and, based on it, to propose the PCM
acceptance approach that is supported by the classical statistical methodology.
Kernelized Deep Convolutional Neural Network for Describing Complex Images
Authors: Zhen Liu
Date: 2015-09-15 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Translation

With the impressive capability to capture visual content, deep convolutional
neural networks (CNN) have demon- strated promising performance in various
vision-based ap- plications, such as classification, recognition, and objec- t
detection. However, due to the intrinsic structure design of CNN, for images
with complex content, it achieves lim- ited capability on invariance to
translation, rotation, and re-sizing changes, which is strongly emphasized in
the s- cenario of content-based image retrieval. In this paper, to address this
problem, we proposed a new kernelized deep convolutional neural network. We
first discuss our motiva- tion by an experimental study to demonstrate the
sensitivi- ty of the global CNN feature to the basic geometric trans-
formations. Then, we propose to represent visual content with approximate
invariance to the above geometric trans- formations from a kernelized
perspective. We extract CNN features on the detected object-like patches and
aggregate these patch-level CNN features to form a vectorial repre- sentation
with the Fisher vector model. The effectiveness of our proposed algorithm is
demonstrated on image search application with three benchmark datasets.
Maximum Correntropy Kalman Filter
Authors: Badong Chen, Xi Liu, Haiquan Zhao, José C. Príncipe
Date: 2015-09-15 00:00:00

Traditional Kalman filter (KF) is derived under the well-known minimum mean
square error (MMSE) criterion, which is optimal under Gaussian assumption.
However, when the signals are non-Gaussian, especially when the system is
disturbed by some heavy-tailed impulsive noises, the performance of KF will
deteriorate seriously. To improve the robustness of KF against impulsive
noises, we propose in this work a new Kalman filter, called the maximum
correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy
criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar
to the traditional KF, the state mean and covariance matrix propagation
equations are used to give prior estimations of the state and covariance matrix
in MCKF. A novel fixed-point algorithm is then used to update the posterior
estimations. A sufficient condition that guarantees the convergence of the
fixed-point algorithm is given. Illustration examples are presented to
demonstrate the effectiveness and robustness of the new algorithm.
When are Kalman-filter restless bandits indexable?
Authors: Christopher R. Dance, Tomi Silander
Date: 2015-09-15 00:00:00
Proceeding: NeurIPS 2015 12

We study the restless bandit associated with an extremely simple scalar
Kalman filter model in discrete time. Under certain assumptions, we prove that
the problem is indexable in the sense that the Whittle index is a
non-decreasing function of the relevant belief state. In spite of the long
history of this problem, this appears to be the first such proof. We use
results about Schur-convexity and mechanical words, which are particular binary
strings intimately related to palindromes.
A Low Complexity VLSI Architecture for Multi-Focus Image Fusion in DCT Domain
Authors: Ashutosh Mishra, Sudipta Mahapatra, Swapna Banerjee
Date: 2015-09-15 00:00:00
Tasks: 4k, Image Compression, Multi Focus Image Fusion

Due to the confined focal length of optical sensors, focusing all objects in
a scene with a single sensor is a difficult task. To handle such a situation,
image fusion methods are used in multi-focus environment. Discrete Cosine
Transform (DCT) is a widely used image compression transform, image fusion in
DCT domain is an efficient method. This paper presents a low complexity
approach for multi-focus image fusion and its VLSI implementation using DCT.
The proposed method is evaluated using reference/non-reference fusion measure
criteria and the obtained results asserts it's effectiveness. The maximum
synthesized frequency on FPGA is found to be 221 MHz and consumes 42% of FPGA
resources. The proposed method consumes very less power and can process 4K
resolution images at the rate of 60 frames per second which makes the hardware
suitable for handheld portable devices such as camera module and wireless image
sensors.
On Reasoning with RDF Statements about Statements using Singleton Property Triples
Authors: Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan, Gang Fu, Evan Bolton, Núria Queralt Rosinach, Laura I. Furlong, Michel Dumontier, Amit Sheth
Date: 2015-09-15 00:00:00

The Singleton Property (SP) approach has been proposed for representing and
querying metadata about RDF triples such as provenance, time, location, and
evidence. In this approach, one singleton property is created to uniquely
represent a relationship in a particular context, and in general, generates a
large property hierarchy in the schema. It has become the subject of important
questions from Semantic Web practitioners. Can an existing reasoner recognize
the singleton property triples? And how? If the singleton property triples
describe a data triple, then how can a reasoner infer this data triple from the
singleton property triples? Or would the large property hierarchy affect the
reasoners in some way? We address these questions in this paper and present our
study about the reasoning aspects of the singleton properties. We propose a
simple mechanism to enable existing reasoners to recognize the singleton
property triples, as well as to infer the data triples described by the
singleton property triples. We evaluate the effect of the singleton property
triples in the reasoning processes by comparing the performance on RDF datasets
with and without singleton properties. Our evaluation uses as benchmark the
LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal
information added through singleton properties.
Splitting Compounds by Semantic Analogy
Authors: Joachim Daiber, Lautaro Quiroz, Roger Wechsler, Stella Frank
Date: 2015-09-15 00:00:00
Proceeding: WS 2015 1
Tasks: Machine Translation, Translation, Word Embeddings

Compounding is a highly productive word-formation process in some languages
that is often problematic for natural language processing applications. In this
paper, we investigate whether distributional semantics in the form of word
embeddings can enable a deeper, i.e., more knowledge-rich, processing of
compounds than the standard string-based methods. We present an unsupervised
approach that exploits regularities in the semantic vector space (based on
analogies such as "bookshop is to shop as bookshelf is to shelf") to produce
compound analyses of high quality. A subsequent compound splitting algorithm
based on these analyses is highly effective, particularly for ambiguous
compounds. German to English machine translation experiments show that this
semantic analogy-based compound splitter leads to better translations than a
commonly used frequency-based method.