pwc-archive/papers-with-abstracts huggingface dataset:
An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization
Authors: Pierre Alquier, Benjamin Guedj
Date: 2016-01-06 00:00:00

The aim of this paper is to provide some theoretical understanding of
quasi-Bayesian aggregation methods non-negative matrix factorization. We derive
an oracle inequality for an aggregated estimator. This result holds for a very
general class of prior distributions and shows how the prior affects the rate
of convergence.
Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification
Authors: Victoria Peterson, Hugo Leonardo Rufiner, Ruben Daniel Spies
Date: 2016-08-24 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), ERP, feature selection, General Classification

A brain computer interface (BCI) is a system which provides direct
communication between the mind of a person and the outside world by using only
brain activity (EEG). The event-related potential (ERP)-based BCI problem
consists of a binary pattern recognition. Linear discriminant analysis (LDA) is
widely used to solve this type of classification problems, but it fails when
the number of features is large relative to the number of observations. In this
work we propose a penalized version of the sparse discriminant analysis (SDA),
called Kullback-Leibler penalized sparse discriminant analysis (KLSDA). This
method inherits both the discriminative feature selection and classification
properties of SDA and it also improves SDA performance through the addition of
Kullback-Leibler class discrepancy information. The KLSDA method is design to
automatically select the optimal regularization parameters. Numerical
experiments with two real ERP-EEG datasets show that this new method
outperforms standard SDA.
Effect of Incomplete Meta-dataset on Average Ranking Method
Authors: Salisu Mamman Abdulrahman, Pavel Brazdil
Date: 2016-08-24 00:00:00

One of the simplest metalearning methods is the average ranking method. This
method uses metadata in the form of test results of a given set of algorithms
on given set of datasets and calculates an average rank for each algorithm. The
ranks are used to construct the average ranking. We investigate the problem of
how the process of generating the average ranking is affected by incomplete
metadata including fewer test results. This issue is relevant, because if we
could show that incomplete metadata does not affect the final results much, we
could explore it in future design. We could simply conduct fewer tests and save
thus computation time. In this paper we describe an upgraded average ranking
method that is capable of dealing with incomplete metadata. Our results show
that the proposed method is relatively robust to omission in test results in
the meta datasets.
In the Saddle: Chasing Fast and Repeatable Features
Authors: Javier Aldana-Iuit, Dmytro Mishkin, Ondrej Chum, Jiri Matas
Date: 2016-08-24 00:00:00

A novel similarity-covariant feature detector that extracts points whose
neighbourhoods, when treated as a 3D intensity surface, have a saddle-like
intensity profile. The saddle condition is verified efficiently by intensity
comparisons on two concentric rings that must have exactly two dark-to-bright
and two bright-to-dark transitions satisfying certain geometric constraints.
Experiments show that the Saddle features are general, evenly spread and
appearing in high density in a range of images. The Saddle detector is among
the fastest proposed. In comparison with detector with similar speed, the
Saddle features show superior matching performance on number of challenging
datasets.
Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Authors: Thomas Kober, Julie Weeds, Jeremy Reffin, David Weir
Date: 2016-08-24 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Semantic Composition, Word Similarity

Distributional models are derived from co-occurrences in a corpus, where only
a small proportion of all possible plausible co-occurrences will be observed.
This results in a very sparse vector space, requiring a mechanism for inferring
missing knowledge. Most methods face this challenge in ways that render the
resulting word representations uninterpretable, with the consequence that
semantic composition becomes hard to model. In this paper we explore an
alternative which involves explicitly inferring unobserved co-occurrences using
the distributional neighbourhood. We show that distributional inference
improves sparse word representations on several word similarity benchmarks and
demonstrate that our model is competitive with the state-of-the-art for
adjective-noun, noun-noun and verb-object compositions while being fully
interpretable.
Expressibility of norms in temporal logic
Authors: Natasha Alechina, Mehdi Dastani, Brian Logan
Date: 2016-08-24 00:00:00

In this short note we address the issue of expressing norms (such as
obligations and prohibitions) in temporal logic. In particular, we address the
argument from [Governatori 2015] that norms cannot be expressed in Linear Time
Temporal Logic (LTL).
A Study of Vision based Human Motion Recognition and Analysis
Authors: Geetanjali Vinayak Kale, Varsha Hemant Patil
Date: 2016-08-24 00:00:00
Tasks: Gesture Recognition

Vision based human motion recognition has fascinated many researchers due to
its critical challenges and a variety of applications. The applications range
from simple gesture recognition to complicated behaviour understanding in
surveillance system. This leads to major development in the techniques related
to human motion representation and recognition. This paper discusses
applications, general framework of human motion recognition, and the details of
each of its components. The paper emphasizes on human motion representation and
the recognition methods along with their advantages and disadvantages. This
study also discusses the selected literature, popular datasets, and concludes
with the challenges in the domain along with a future direction. The human
motion recognition domain has been active for more than two decades, and has
provided a large amount of literature. A bird's eye view for new researchers in
the domain is presented in the paper.
Does V-NIR based Image Enhancement Come with Better Features?
Authors: Vivek Sharma, Luc van Gool
Date: 2016-08-23 00:00:00
Tasks: Image Enhancement

Image enhancement using the visible (V) and near-infrared (NIR) usually
enhances useful image details. The enhanced images are evaluated by observers
perception, instead of quantitative feature evaluation. Thus, can we say that
these enhanced images using NIR information has better features in comparison
to the computed features in the Red, Green, and Blue color channels directly?
In this work, we present a new method to enhance the visible images using NIR
information via edge-preserving filters, and also investigate which method
performs best from a image features standpoint. We then show that our proposed
enhancement method produces more stable features than the existing
state-of-the-art methods.
Robust Named Entity Recognition in Idiosyncratic Domains
Authors: Sebastian Arnold, Felix A. Gers, Torsten Kilias, Alexander Löser
Date: 2016-08-24 00:00:00
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction

Named entity recognition often fails in idiosyncratic domains. That causes a
problem for depending tasks, such as entity linking and relation extraction. We
propose a generic and robust approach for high-recall named entity recognition.
Our approach is easy to train and offers strong generalization over diverse
domain-specific language, such as news documents (e.g. Reuters) or biomedical
text (e.g. Medline). Our approach is based on deep contextual sequence learning
and utilizes stacked bidirectional LSTM networks. Our model is trained with
only few hundred labeled sentences and does not rely on further external
knowledge. We report from our results F1 scores in the range of 84-94% on
standard datasets.
Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Authors: Jason L. Williams
Date: 2012-03-14 00:00:00

Recent developments in random finite sets (RFSs) have yielded a variety of
tracking methods that avoid data association. This paper derives a form of the
full Bayes RFS filter and observes that data association is implicitly present,
in a data structure similar to MHT. Subsequently, algorithms are obtained by
approximating the distribution of associations. Two algorithms result: one
nearly identical to JIPDA, and another related to the MeMBer filter. Both
improve performance in challenging environments.
A Large-Scale Multilingual Disambiguation of Glosses
Authors: José Camacho Collados, Claudio Delli Bovi, Alessandro Raganato, Roberto Navigli
Date: 2016-08-24 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Natural Language Understanding, Open Information Extraction, Semantic Similarity, Semantic Textual Similarity

Linking concepts and named entities to knowledge bases has become a crucial
Natural Language Understanding task. In this respect, recent works have shown
the key advantage of exploiting textual definitions in various Natural Language
Processing applications. However, to date there are no reliable large-scale
corpora of sense-annotated textual definitions available to the research
community. In this paper we present a large-scale high-quality corpus of
disambiguated glosses in multiple languages, comprising sense annotations of
both concepts and named entities from a unified sense inventory. Our approach
for the construction and disambiguation of the corpus builds upon the structure
of a large multilingual semantic network and a state-of-the-art disambiguation
system; first, we gather complementary information of equivalent definitions
across different languages to provide context for disambiguation, and then we
combine it with a semantic similarity-based refinement. As a result we obtain a
multilingual corpus of textual definitions featuring over 38 million
definitions in 263 languages, and we make it freely available at
http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information
Extraction and Sense Clustering show how two state-of-the-art approaches
improve their performance by integrating our disambiguated corpus into their
pipeline.
A Novel Approach for Shot Boundary Detection in Videos
Authors: D. S. Guru, Mahamad Suhil, P. Lolika
Date: 2016-08-24 00:00:00
Tasks: Boundary Detection, Clustering

This paper presents a novel approach for video shot boundary detection. The
proposed approach is based on split and merge concept. A fisher linear
discriminant criterion is used to guide the process of both splitting and
merging. For the purpose of capturing the between class and within class
scatter we employ 2D2 FLD method which works on texture feature of regions in
each frame of a video. Further to reduce the complexity of the process we
propose to employ spectral clustering to group related regions together to a
single there by achieving reduction in dimension. The proposed method is
experimentally also validated on a cricket video. It is revealed that shots
obtained by the proposed approach are highly cohesive and loosely coupled
Transfer Learning for Endoscopic Image Classification
Authors: Shoji Sonoyama, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
Date: 2016-08-24 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

In this paper we propose a method for transfer learning of endoscopic images.
For transferring between features obtained from images taken by different (old
and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed
by Hoffman et al. in order to use L2 distance constraints as regularization,
called Max-Margin Domain Transfer with L2 Distance Constraints (MMDTL2).
Furthermore, we develop the dual formulation of the optimization problem in
order to reduce the computation cost. Experimental results demonstrate that the
proposed MMDTL2 outperforms MMDT for real data sets taken by different
endoscopes.
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Authors: Toru Tamaki, Shoji Sonoyama, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
Date: 2016-08-24 00:00:00
Tasks: General Classification

In this paper we report results for recognizing colorectal NBI endoscopic
images by using features extracted from convolutional neural network (CNN). In
this comparative study, we extract features from different layers from
different CNN models, and then train linear SVM classifiers. Experimental
results with 10-fold cross validations show that features from first few
convolution layers are enough to achieve similar performance (i.e., recognition
rate of 95%) with non-CNN local features such as Bag-of-Visual words, Fisher
vector, and VLAD.
Semantic descriptions of 24 evaluational adjectives, for application in sentiment analysis
Authors: Cliff Goddard, Maite Taboada, Radoslava Trnavac
Date: 2016-08-24 00:00:00
Tasks: Sentiment Analysis

We apply the Natural Semantic Metalanguage (NSM) approach (Goddard and
Wierzbicka 2014) to the lexical-semantic analysis of English evaluational
adjectives and compare the results with the picture developed in the Appraisal
Framework (Martin and White 2005). The analysis is corpus-assisted, with
examples mainly drawn from film and book reviews, and supported by
collocational and statistical information from WordBanks Online. We propose NSM
explications for 24 evaluational adjectives, arguing that they fall into five
groups, each of which corresponds to a distinct semantic template. The groups
can be sketched as follows: "First-person thought-plus-affect", e.g. wonderful;
"Experiential", e.g. entertaining; "Experiential with bodily reaction", e.g.
gripping; "Lasting impact", e.g. memorable; "Cognitive evaluation", e.g.
complex, excellent. These groupings and semantic templates are compared with
the classifications in the Appraisal Framework's system of Appreciation. In
addition, we are particularly interested in sentiment analysis, the automatic
identification of evaluation and subjectivity in text. We discuss the relevance
of the two frameworks for sentiment analysis and other language technology
applications.
Neural Universal Discrete Denoiser
Authors: Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
Date: 2016-05-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Denoising

We present a new framework of applying deep neural networks (DNN) to devise a
universal discrete denoiser. Unlike other approaches that utilize supervised
learning for denoising, we do not require any additional training data. In such
setting, while the ground-truth label, i.e., the clean data, is not available,
we devise "pseudo-labels" and a novel objective function such that DNN can be
trained in a same way as supervised learning to become a discrete denoiser. We
experimentally show that our resulting algorithm, dubbed as Neural DUDE,
significantly outperforms the previous state-of-the-art in several applications
with a systematic rule of choosing the hyperparameter, which is an attractive
feature in practice.
Computerized Tomography with Total Variation and with Shearlets
Authors: Edgar Garduño, Gabor T. Herman
Date: 2016-08-23 00:00:00

To reduce the x-ray dose in computerized tomography (CT), many constrained
optimization approaches have been proposed aiming at minimizing a regularizing
function that measures lack of consistency with some prior knowledge about the
object that is being imaged, subject to a (predetermined) level of consistency
with the detected attenuation of x-rays. Proponents of the shearlet transform
in the regularizing function claim that the reconstructions so obtained are
better than those produced using TV for texture preservation (but may be worse
for noise reduction). In this paper we report results related to this claim. In
our reported experiments using simulated CT data collection of the head,
reconstructions whose shearlet transform has a small $\ell_1$-norm are not more
efficacious than reconstructions that have a small TV value. Our experiments
for making such comparisons use the recently-developed superiorization
methodology for both regularizing functions. Superiorization is an automated
procedure for turning an iterative algorithm for producing images that satisfy
a primary criterion (such as consistency with the observed measurements) into
its superiorized version that will produce results that, according to the
primary criterion are as good as those produced by the original algorithm, but
in addition are superior to them according to a secondary (regularizing)
criterion. The method presented for superiorization involving the $\ell_1$-norm
of the shearlet transform is novel and is quite general: It can be used for any
regularizing function that is defined as the $\ell_1$-norm of a transform
specified by the application of a matrix. Because in the previous literature
the split Bregman algorithm is used for similar purposes, a section is included
comparing the results of the superiorization algorithm with the split Bregman
algorithm.
Deep learning is competing random forest in computational docking
Authors: Mohamed Khamis, Walid Gomaa, Basem Galal
Date: 2016-08-23 00:00:00
Tasks: Deep Learning, Drug Design

Computational docking is the core process of computer-aided drug design; it
aims at predicting the best orientation and conformation of a small drug
molecule when bound to a target large protein receptor. The docking quality is
typically measured by a scoring function: a mathematical predictive model that
produces a score representing the binding free energy and hence the stability
of the resulting complex molecule. We analyze the performance of both learning
techniques on the scoring power, the ranking power, docking power, and
screening power using the PDBbind 2013 database. For the scoring and ranking
powers, the proposed learning scoring functions depend on a wide range of
features (energy terms, pharmacophore, intermolecular) that entirely
characterize the protein-ligand complexes. For the docking and screening
powers, the proposed learning scoring functions depend on the intermolecular
features of the RF-Score to utilize a larger number of training complexes. For
the scoring power, the DL\_RF scoring function achieves Pearson's correlation
coefficient between the predicted and experimentally measured binding
affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking
power, the DL scoring function ranks the ligands bound to fixed target protein
with accuracy 54% for the high-level ranking and with accuracy 78% for the
low-level ranking while the RF scoring function achieves (46% and 62%)
respectively. For the docking power, the DL\_RF scoring function has a success
rate when the three best-scored ligand binding poses are considered within 2
\AA\ root-mean-square-deviation from the native pose of 36.0% versus 30.2% of
the RF scoring function. For the screening power, the DL scoring function has
an average enrichment factor and success rate at the top 1% level of (2.69 and
6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoring
function.
Topic Grids for Homogeneous Data Visualization
Authors: Shih-Chieh Su, Joseph Vaughn, Jean-Laurent Huynh
Date: 2016-08-23 00:00:00
Tasks: Data Visualization

We propose the topic grids to detect anomaly and analyze the behavior based
on the access log content. Content-based behavioral risk is quantified in the
high dimensional space where the topics are generated from the log. The topics
are being projected homogeneously into a space that is perception- and
interaction-friendly to the human experts.
Lexical Query Modeling in Session Search
Authors: Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke
Date: 2016-08-23 00:00:00
Tasks: Session Search

Lexical query modeling has been the leading paradigm for session search. In
this paper, we analyze TREC session query logs and compare the performance of
different lexical matching approaches for session search. Naive methods based
on term frequency weighing perform on par with specialized session models. In
addition, we investigate the viability of lexical query models in the setting
of session search. We give important insights into the potential and
limitations of lexical query modeling for session search and propose future
directions for the field of session search.
Self-Averaging Expectation Propagation
Authors: Burak Çakmak, Manfred Opper, Bernard H. Fleury, Ole Winther
Date: 2016-08-23 00:00:00
Tasks: Bayesian Inference, compressed sensing

We investigate the problem of approximate Bayesian inference for a general
class of observation models by means of the expectation propagation (EP)
framework for large systems under some statistical assumptions. Our approach
tries to overcome the numerical bottleneck of EP caused by the inversion of
large matrices. Assuming that the measurement matrices are realizations of
specific types of ensembles we use the concept of freeness from random matrix
theory to show that the EP cavity variances exhibit an asymptotic
self-averaging property. They can be pre-computed using specific generating
functions, i.e. the R- and/or S-transforms in free probability, which do not
require matrix inversions. Our approach extends the framework of (generalized)
approximate message passing -- assumes zero-mean iid entries of the measurement
matrix -- to a general class of random matrix ensembles. The generalization is
via a simple formulation of the R- and/or S-transforms of the limiting
eigenvalue distribution of the Gramian of the measurement matrix. We
demonstrate the performance of our approach on a signal recovery problem of
nonlinear compressed sensing and compare it with that of EP.
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization
Authors: Quoc Tran-Dinh
Date: 2016-06-10 00:00:00
Tasks: Matrix Completion

In this paper, we develop a variant of the well-known Gauss-Newton (GN) method to solve a class of nonconvex optimization problems involving low-rank matrix variables. As opposed to the standard GN method, our algorithm allows one to handle general smooth convex objective function. We show, under mild conditions, that the proposed algorithm globally and locally converges to a stationary point of the original problem. We also show empirically that the GN algorithm achieves higher accurate solutions than the alternating minimization algorithm (AMA). Then, we specify our GN scheme to handle the symmetric case and prove its convergence, where AMA is not applicable. Next, we incorporate our GN scheme into the alternating direction method of multipliers (ADMM) to develop an ADMM-GN algorithm. We prove that, under mild conditions and a proper choice of the penalty parameter, our ADMM-GN globally converges to a stationary point of the original problem. Finally, we provide several numerical experiments to illustrate the proposed algorithms. Our results show that the new algorithms have encouraging performance compared to existing methods.