pwc-archive/papers-with-abstracts huggingface dataset:
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
Authors: Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll, Annika Plate, Verena Rozanski, Juliana Maiostre, Johannes Levin, Olaf Dietrich, Birgit Ertl-Wagner, Kai Bötzel, Nassir Navab
Date: 2016-01-26 00:00:00
Tasks: Medical Image Analysis, Segmentation

In this work we propose a novel approach to perform segmentation by
leveraging the abstraction capabilities of convolutional neural networks
(CNNs). Our method is based on Hough voting, a strategy that allows for fully
automatic localisation and segmentation of the anatomies of interest. This
approach does not only use the CNN classification outcomes, but it also
implements voting by exploiting the features produced by the deepest portion of
the network. We show that this learning-based segmentation method is robust,
multi-region, flexible and can be easily adapted to different modalities. In
the attempt to show the capabilities and the behaviour of CNNs when they are
applied to medical image analysis, we perform a systematic study of the
performances of six different network architectures, conceived according to
state-of-the-art criteria, in various situations. We evaluate the impact of
both different amount of training data and different data dimensionality (2D,
2.5D and 3D) on the final results. We show results on both MRI and transcranial
US volumes depicting respectively 26 regions of the basal ganglia and the
midbrain.
WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter
Authors: Suman Kalyan Maity, Chaitanya Sarda, Anshit Chaudhary, Abhijeet Patil, Shraman Kumar, Akash Mondal, Animesh Mukherjee
Date: 2016-01-31 00:00:00
Tasks: General Classification

Language in social media is mostly driven by new words and spellings that are
constantly entering the lexicon thereby polluting it and resulting in high
deviation from the formal written version. The primary entities of such
language are the out-of-vocabulary (OOV) words. In this paper, we study various
sociolinguistic properties of the OOV words and propose a classification model
to categorize them into at least six categories. We achieve 81.26% accuracy
with high precision and recall. We observe that the content features are the
most discriminative ones followed by lexical and context features.
Numerical Atrribute Extraction from Clinical Texts
Authors: Sarath P R, Sunil Mandhan, Yoshiki Niwa
Date: 2016-01-31 00:00:00
Tasks: Attribute, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation, Relation Extraction

This paper describes about information extraction system, which is an extension of the system developed by team Hitachi for "Disease/Disorder Template filling" task organized by ShARe/CLEF eHealth Evolution Lab 2014. In this extension module we focus on extraction of numerical attributes and values from discharge summary records and associating correct relation between attributes and values. We solve the problem in two steps. First step is extraction of numerical attributes and values, which is developed as a Named Entity Recognition (NER) model using Stanford NLP libraries. Second step is correctly associating the attributes to values, which is developed as a relation extraction module in Apache cTAKES framework. We integrated Stanford NER model as cTAKES pipeline component and used in relation extraction module. Conditional Random Field (CRF) algorithm is used for NER and Support Vector Machines (SVM) for relation extraction. For attribute value relation extraction, we observe 95% accuracy using NER alone and combined accuracy of 87% with NER and SVM.
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
Authors: Valero Laparra, Sandra Jiménez, Gustavo Camps-Valls, Jesús Malo
Date: 2016-01-31 00:00:00

Mechanisms of human color vision are characterized by two phenomenological
aspects: the system is nonlinear and adaptive to changing environments.
Conventional attempts to derive these features from statistics use separate
arguments for each aspect. The few statistical approaches that do consider both
phenomena simultaneously follow parametric formulations based on empirical
models. Therefore, it may be argued that the behavior does not come directly
from the color statistics but from the convenient functional form adopted. In
addition, many times the whole statistical analysis is based on simplified
databases that disregard relevant physical effects in the input signal, as for
instance by assuming flat Lambertian surfaces. Here we address the simultaneous
statistical explanation of (i) the nonlinear behavior of achromatic and
chromatic mechanisms in a fixed adaptation state, and (ii) the change of such
behavior. Both phenomena emerge directly from the samples through a single
data-driven method: the Sequential Principal Curves Analysis (SPCA) with local
metric. SPCA is a new manifold learning technique to derive a set of sensors
adapted to the manifold using different optimality criteria. A new database of
colorimetrically calibrated images of natural objects under these illuminants
was collected. The results obtained by applying SPCA show that the
psychophysical behavior on color discrimination thresholds, discount of the
illuminant and corresponding pairs in asymmetric color matching, emerge
directly from realistic data regularities assuming no a priori functional form.
These results provide stronger evidence for the hypothesis of a statistically
driven organization of color sensors. Moreover, the obtained results suggest
that color perception at this low abstraction level may be guided by an error
minimization strategy rather than by the information maximization principle.
Iterative Gaussianization: from ICA to Random Rotations
Authors: Valero Laparra, Gustavo Camps-Valls, Jesús Malo
Date: 2016-01-31 00:00:00
Proceeding: IEEE Transactions on Neural Networks 2011 2
Tasks: Denoising, Image Generation

Most signal processing problems involve the challenging task of
multidimensional probability density function (PDF) estimation. In this work,
we propose a solution to this problem by using a family of Rotation-based
Iterative Gaussianization (RBIG) transforms. The general framework consists of
the sequential application of a univariate marginal Gaussianization transform
followed by an orthonormal transform. The proposed procedure looks for
differentiable transforms to a known PDF so that the unknown PDF can be
estimated at any point of the original domain. In particular, we aim at a zero
mean unit covariance Gaussian for convenience. RBIG is formally similar to
classical iterative Projection Pursuit (PP) algorithms. However, we show that,
unlike in PP methods, the particular class of rotations used has no special
qualitative relevance in this context, since looking for interestingness is not
a critical issue for PDF estimation. The key difference is that our approach
focuses on the univariate part (marginal Gaussianization) of the problem rather
than on the multivariate part (rotation). This difference implies that one may
select the most convenient rotation suited to each practical application. The
differentiability, invertibility and convergence of RBIG are theoretically and
experimentally analyzed. Relation to other methods, such as Radial
Gaussianization (RG), one-class support vector domain description (SVDD), and
deep neural networks (DNN) is also pointed out. The practical performance of
RBIG is successfully illustrated in a number of multidimensional problems such
as image synthesis, classification, denoising, and multi-information
estimation.
Order-aware Convolutional Pooling for Video Based Action Recognition
Authors: Peng Wang, Lingqiao Liu, Chunhua Shen, Heng Tao Shen
Date: 2016-01-31 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Most video based action recognition approaches create the video-level
representation by temporally pooling the features extracted at each frame. The
pooling methods that they adopt, however, usually completely or partially
neglect the dynamic information contained in the temporal domain, which may
undermine the discriminative power of the resulting video representation since
the video sequence order could unveil the evolution of a specific event or
action. To overcome this drawback and explore the importance of incorporating
the temporal order information, in this paper we propose a novel temporal
pooling approach to aggregate the frame-level features. Inspired by the
capacity of Convolutional Neural Networks (CNN) in making use of the internal
structure of images for information abstraction, we propose to apply the
temporal convolution operation to the frame-level representations to extract
the dynamic information. However, directly implementing this idea on the
original high-dimensional feature would inevitably result in parameter
explosion.
  To tackle this problem, we view the temporal evolution of the feature value
at each feature dimension as a 1D signal and learn a unique convolutional
filter bank for each of these 1D signals. We conduct experiments on two
challenging video-based action recognition datasets, HMDB51 and UCF101; and
demonstrate that the proposed method is superior to the conventional pooling
methods.
Principal Polynomial Analysis
Authors: Valero Laparra, Sandra Jiménez, Devis Tuia, Gustau Camps-Valls, Jesús Malo
Date: 2016-01-31 00:00:00
Tasks: Dimensionality Reduction

This paper presents a new framework for manifold learning based on a sequence
of principal polynomials that capture the possibly nonlinear nature of the
data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by
modeling the directions of maximal variance by means of curves, instead of
straight lines. Contrarily to previous approaches, PPA reduces to performing
simple univariate regressions, which makes it computationally feasible and
robust. Moreover, PPA shows a number of interesting analytical properties.
First, PPA is a volume-preserving map, which in turn guarantees the existence
of the inverse. Second, such an inverse can be obtained in closed form.
Invertibility is an important advantage over other learning methods, because it
permits to understand the identified features in the input domain where the
data has physical meaning. Moreover, it allows to evaluate the performance of
dimensionality reduction in sensible (input-domain) units. Volume preservation
also allows an easy computation of information theoretic quantities, such as
the reduction in multi-information after the transform. Third, the analytical
nature of PPA leads to a clear geometrical interpretation of the manifold: it
allows the computation of Frenet-Serret frames (local features) and of
generalized curvatures at any point of the space. And fourth, the analytical
Jacobian allows the computation of the metric induced by the data, thus
generalizing the Mahalanobis distance. These properties are demonstrated
theoretically and illustrated experimentally. The performance of PPA is
evaluated in dimensionality and redundancy reduction, in both synthetic and
real datasets from the UCI repository.
Image Denoising with Kernels based on Natural Image Relations
Authors: Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo
Date: 2016-01-31 00:00:00
Tasks: Denoising, Image Denoising

A successful class of image denoising methods is based on Bayesian approaches
working in wavelet representations. However, analytical estimates can be
obtained only for particular combinations of analytical models of signal and
noise, thus precluding its straightforward extension to deal with other
arbitrary noise sources. In this paper, we propose an alternative non-explicit
way to take into account the relations among natural image wavelet coefficients
for denoising: we use support vector regression (SVR) in the wavelet domain to
enforce these relations in the estimated signal. Since relations among the
coefficients are specific to the signal, the regularization property of SVR is
exploited to remove the noise, which does not share this feature. The specific
signal relations are encoded in an anisotropic kernel obtained from mutual
information measures computed on a representative image database. Training
considers minimizing the Kullback-Leibler divergence (KLD) between the
estimated and actual probability functions of signal and noise in order to
enforce similarity. Due to its non-parametric nature, the method can eventually
cope with different noise sources without the need of an explicit
re-formulation, as it is strictly necessary under parametric Bayesian
formalisms. Results under several noise levels and noise sources show that: (1)
the proposed method outperforms conventional wavelet methods that assume
coefficient independence, (2) it is similar to state-of-the-art methods that do
explicitly include these relations when the noise source is Gaussian, and (3)
it gives better numerical and visual performance when more complex, realistic
noise sources are considered. Therefore, the proposed machine learning approach
can be seen as a more flexible (model-free) alternative to the explicit
description of wavelet coefficient relations for image denoising.
Dimensionality Reduction via Regression in Hyperspectral Imagery
Authors: Valero Laparra, Jesus Malo, Gustau Camps-Valls
Date: 2016-01-31 00:00:00
Tasks: Dimensionality Reduction, Land Cover Classification, regression

This paper introduces a new unsupervised method for dimensionality reduction
via regression (DRR). The algorithm belongs to the family of invertible
transforms that generalize Principal Component Analysis (PCA) by using
curvilinear instead of linear features. DRR identifies the nonlinear features
through multivariate regression to ensure the reduction in redundancy between
he PCA coefficients, the reduction of the variance of the scores, and the
reduction in the reconstruction error. More importantly, unlike other nonlinear
dimensionality reduction methods, the invertibility, volume-preservation, and
straightforward out-of-sample extension, makes DRR interpretable and easy to
apply. The properties of DRR enable learning a more broader class of data
manifolds than the recently proposed Non-linear Principal Components Analysis
(NLPCA) and Principal Polynomial Analysis (PPA). We illustrate the performance
of the representation in reducing the dimensionality of remote sensing data. In
particular, we tackle two common problems: processing very high dimensional
spectral information such as in hyperspectral image sounding data, and dealing
with spatial-spectral image patches of multispectral images. Both settings pose
collinearity and ill-determination problems. Evaluation of the expressive power
of the features is assessed in terms of truncation error, estimating
atmospheric variables, and surface land cover classification error. Results
show that DRR outperforms linear PCA and recently proposed invertible
extensions based on neural networks (NLPCA) and univariate regressions (PPA).
Unsupervised Deep Hashing for Large-scale Visual Search
Authors: Zhaoqiang Xia, Xiaoyi Feng, Jinye Peng, Abdenour Hadid
Date: 2016-01-31 00:00:00
Tasks: Deep Hashing

Learning based hashing plays a pivotal role in large-scale visual search.
However, most existing hashing algorithms tend to learn shallow models that do
not seek representative binary codes. In this paper, we propose a novel hashing
approach based on unsupervised deep learning to hierarchically transform
features into hash codes. Within the heterogeneous deep hashing framework, the
autoencoder layers with specific constraints are considered to model the
nonlinear mapping between features and binary codes. Then, a Restricted
Boltzmann Machine (RBM) layer with constraints is utilized to reduce the
dimension in the hamming space. Extensive experiments on the problem of visual
search demonstrate the competitiveness of our proposed approach compared to
state-of-the-art.
Greedy Deep Dictionary Learning
Authors: Snigdha Tariyal, Angshul Majumdar, Richa Singh, Mayank Vatsa
Date: 2016-01-31 00:00:00
Tasks: Deep Learning, Dictionary Learning

In this work we propose a new deep learning tool called deep dictionary
learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at
a time. This requires solving a simple (shallow) dictionary learning problem,
the solution to this is well known. We apply the proposed technique on some
benchmark deep learning datasets. We compare our results with other deep
learning tools like stacked autoencoder and deep belief network; and state of
the art supervised dictionary learning tools like discriminative KSVD and label
consistent KSVD. Our method yields better results than all.
Discussion on Mechanical Learning and Learning Machine
Authors: Chuyu Xiong
Date: 2016-01-31 00:00:00

Mechanical learning is a computing system that is based on a set of simple
and fixed rules, and can learn from incoming data. A learning machine is a
system that realizes mechanical learning. Importantly, we emphasis that it is
based on a set of simple and fixed rules, contrasting to often called machine
learning that is sophisticated software based on very complicated mathematical
theory, and often needs human intervene for software fine tune and manual
adjustments. Here, we discuss some basic facts and principles of such system,
and try to lay down a framework for further study. We propose 2 directions to
approach mechanical learning, just like Church-Turing pair: one is trying to
realize a learning machine, another is trying to well describe the mechanical
learning.
Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach
Authors: Sagi Eppel
Date: 2016-01-31 00:00:00

Detection of boundaries of materials stored in transparent vessels is
essential for identifying properties such as liquid level and phase boundaries,
which are vital for controlling numerous processes in the industry and
chemistry laboratory. This work presents a computer vision method for
identifying the boundary of materials in transparent vessels using the
graph-cut algorithm. The method receives an image of a transparent vessel
containing a material and the contour of the vessel in the image. The boundary
of the material in the vessel is found by the graph cut method. In general the
method uses the vessel region of the image to create a graph, where pixels are
vertices, and the cost of an edge between two pixels is inversely correlated
with their intensity difference. The bottom 10% of the vessel region in the
image is assumed to correspond to the material phase and defined as the graph
and source. The top 10% of the pixels in the vessels are assumed to correspond
to the air phase and defined as the graph sink. The minimal cut that splits the
resulting graph between the source and sink (hence, material and air) is traced
using the max-flow/min-cut approach. This cut corresponds to the boundary of
the material in the image. The method gave high accuracy in boundary
recognition for a wide range of liquid, solid, granular and powder materials in
various glass vessels from everyday life and the chemistry laboratory, such as
bottles, jars, Glasses, Chromotography colums and separatory funnels.
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version
Authors: Amulya Yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe
Date: 2016-01-30 00:00:00

This paper presents HEALER, a software agent that recommends sequential
intervention plans for use by homeless shelters, who organize these
interventions to raise awareness about HIV among homeless youth. HEALER's
sequential plans (built using knowledge of social networks of homeless youth)
choose intervention participants strategically to maximize influence spread,
while reasoning about uncertainties in the network. While previous work
presents influence maximizing techniques to choose intervention participants,
they do not address three real-world issues: (i) they completely fail to scale
up to real-world sizes; (ii) they do not handle deviations in execution of
intervention plans; (iii) constructing real-world social networks is an
expensive process. HEALER handles these issues via four major contributions:
(i) HEALER casts this influence maximization problem as a POMDP and solves it
using a novel planner which scales up to previously unsolvable real-world
sizes; (ii) HEALER allows shelter officials to modify its recommendations, and
updates its future plans in a deviation-tolerant manner; (iii) HEALER
constructs social networks of homeless youth at low cost, using a Facebook
application. Finally, (iv) we show hardness results for the problem that HEALER
solves. HEALER will be deployed in the real world in early Spring 2016 and is
currently undergoing testing at a homeless shelter.
Multiple instance learning for sequence data with across bag dependencies
Authors: Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo
Date: 2016-01-30 00:00:00
Tasks: Classification, General Classification, Multiple Instance Learning

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory.
DNA-inspired online behavioral modeling and its application to spambot detection
Authors: Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi
Date: 2016-01-30 00:00:00
Tasks: DNA analysis

We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks.
Extracting Keyword for Disambiguating Name Based on the Overlap Principle
Authors: Mahyuddin K. M. Nasution
Date: 2016-01-30 00:00:00

Name disambiguation has become one of the main themes in the Semantic Web
agenda. The semantic web is an extension of the current Web in which
information is not only given well-defined meaning, but also has many purposes
that contain the ambiguous naturally or a lot of thing came with the overlap,
mainly deals with the persons name. Therefore, we develop an approach to
extract keywords from web snippet with utilizing the overlap principle, a
concept to understand things with ambiguous, whereby features of person are
generated for dealing with the variety of web, the web is steadily gaining
ground in the semantic research.