pwc-archive/papers-with-abstracts huggingface dataset:
Computational Model for Predicting Visual Fixations from Childhood to Adulthood
Authors: Olivier Le Meur, Antoine Coutrot, Zhi Liu, Adrien Le Roch, Andrea Helo, Pia Rama
Date: 2017-02-15 00:00:00

How people look at visual information reveals fundamental information about
themselves, their interests and their state of mind. While previous visual
attention models output static 2-dimensional saliency maps, saccadic models aim
to predict not only where observers look at but also how they move their eyes
to explore the scene. Here we demonstrate that saccadic models are a flexible
framework that can be tailored to emulate observer's viewing tendencies. More
specifically, we use the eye data from 101 observers split in 5 age groups
(adults, 8-10 y.o., 6-8 y.o., 4-6 y.o. and 2 y.o.) to train our saccadic model
for different stages of the development of the human visual system. We show
that the joint distribution of saccade amplitude and orientation is a visual
signature specific to each age group, and can be used to generate age-dependent
scanpaths. Our age-dependent saccadic model not only outputs human-like,
age-specific visual scanpath, but also significantly outperforms other
state-of-the-art saliency models. In this paper, we demonstrate that the
computational modelling of visual attention, through the use of saccadic model,
can be efficiently adapted to emulate the gaze behavior of a specific group of
observers.
Robust Regression via Mutivariate Regression Depth
Authors: Chao Gao
Date: 2017-02-15 00:00:00
Tasks: regression

This paper studies robust regression in the settings of Huber's
$\epsilon$-contamination models. We consider estimators that are maximizers of
multivariate regression depth functions. These estimators are shown to achieve
minimax rates in the settings of $\epsilon$-contamination models for various
regression problems including nonparametric regression, sparse linear
regression, reduced rank regression, etc. We also discuss a general notion of
depth function for linear operators that has potential applications in robust
functional linear regression.
Filling missing data in point clouds by merging structured and unstructured point clouds
Authors: Franziska Lippoldt, Hartmut Schwandt
Date: 2017-02-15 00:00:00
Tasks: Surface Reconstruction

Point clouds arising from structured data, mainly as a result of CT scans,
provides special properties on the distribution of points and the distances
between those. Yet often, the amount of data provided can not compare to
unstructured point clouds, i.e. data that arises from 3D light scans or laser
scans. This article hereby proposes an approach to extend structured data and
enhance the quality by inserting selected points from an unstructured point
cloud. The resulting point cloud still has a partial structure that is called
"half-structure". In this way, missing data that can not be optimally recovered
through other surface reconstruction methods can be completed.
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Authors: Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling
Date: 2017-02-15 00:00:00
Tasks: Decision Making, Prediction

This article presents the prediction difference analysis method for
visualizing the response of a deep neural network to a specific input. When
classifying images, the method highlights areas in a given input image that
provide evidence for or against a certain class. It overcomes several
shortcoming of previous methods and provides great additional insight into the
decision making process of classifiers. Making neural network decisions
interpretable through visualization is important both to improve models and to
accelerate the adoption of black-box classifiers in application areas such as
medicine. We illustrate the method in experiments on natural images (ImageNet
data), as well as medical images (MRI brain scans).
Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function
Authors: Yiyuan Wang, Shaowei Cai, Minghao Yin
Date: 2017-02-15 00:00:00

The Minimum Weight Dominating Set (MWDS) problem is an important
generalization of the Minimum Dominating Set (MDS) problem with extensive
applications. This paper proposes a new local search algorithm for the MWDS
problem, which is based on two new ideas. The first idea is a heuristic called
two-level configuration checking (CC2), which is a new variant of a recent
powerful configuration checking strategy (CC) for effectively avoiding the
recent search paths. The second idea is a novel scoring function based on the
frequency of being uncovered of vertices. Our algorithm is called CC2FS,
according to the names of the two ideas. The experimental results show that,
CC2FS performs much better than some state-of-the-art algorithms in terms of
solution quality on a broad range of MWDS benchmarks.
Developing an ontology for the access to the contents of an archival fonds: the case of the Catasto Gregoriano
Authors: Lina Antonietta Coppola
Date: 2017-02-15 00:00:00

The research was proposed to exploit and extend the relational and contextual
nature of the information assets of the Catasto Gregoriano, kept at the
Archivio di Stato in Rome. Developed within the MODEUS project (Making Open
Data Effectively Usable), this study originates from the following key ideas of
MODEUS: to require Open Data to be expressed in terms of an ontology, and to
include such an ontology as a documentation of the data themselves. Thus, Open
Data are naturally linked by means of the ontology, which meets the
requirements of the Linked Open Data vision.
Normalized Total Gradient: A New Measure for Multispectral Image Registration
Authors: Shu-Jie Chen, Hui-Liang Shen
Date: 2017-02-15 00:00:00
Tasks: Image Registration

Image registration is a fundamental issue in multispectral image processing.
In filter wheel based multispectral imaging systems, the non-coplanar placement
of the filters always causes the misalignment of multiple channel images. The
selective characteristic of spectral response in multispectral imaging raises
two challenges to image registration. First, the intensity levels of a local
region may be different in individual channel images. Second, the local
intensity may vary rapidly in some channel images while keeps stationary in
others. Conventional multimodal measures, such as mutual information,
correlation coefficient, and correlation ratio, can register images with
different regional intensity levels, but will fail in the circumstance of
severe local intensity variation. In this paper, a new measure, namely
normalized total gradient (NTG), is proposed for multispectral image
registration. The NTG is applied on the difference between two channel images.
This measure is based on the key assumption (observation) that the gradient of
difference image between two aligned channel images is sparser than that
between two misaligned ones. A registration framework, which incorporates image
pyramid and global/local optimization, is further introduced for rigid
transform. Experimental results validate that the proposed method is effective
for multispectral image registration and performs better than conventional
methods.
Probing for sparse and fast variable selection with model-based boosting
Authors: Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl
Date: 2017-02-15 00:00:00
Tasks: Variable Selection

We present a new variable selection method based on model-based gradient
boosting and randomly permuted variables. Model-based boosting is a tool to fit
a statistical model while performing variable selection at the same time. A
drawback of the fitting lies in the need of multiple model fits on slightly
altered data (e.g. cross-validation or bootstrap) to find the optimal number of
boosting iterations and prevent overfitting. In our proposed approach, we
augment the data set with randomly permuted versions of the true variables, so
called shadow variables, and stop the step-wise fitting as soon as such a
variable would be added to the model. This allows variable selection in a
single fit of the model without requiring further parameter tuning. We show
that our probing approach can compete with state-of-the-art selection methods
like stability selection in a high-dimensional classification benchmark and
apply it on gene expression data for the estimation of riboflavin production of
Bacillus subtilis.
Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds
Authors: Florian Bernard, Luis Salamanca, Johan Thunberg, Alexander Tack, Dennis Jentsch, Hans Lamecker, Stefan Zachow, Frank Hertel, Jorge Goncalves, Peter Gemmar
Date: 2016-02-26 00:00:00
Tasks: Anatomy, Medical Image Analysis, Surface Reconstruction

The reconstruction of an object's shape or surface from a set of 3D points
plays an important role in medical image analysis, e.g. in anatomy
reconstruction from tomographic measurements or in the process of aligning
intra-operative navigation and preoperative planning data. In such scenarios,
one usually has to deal with sparse data, which significantly aggravates the
problem of reconstruction. However, medical applications often provide
contextual information about the 3D point data that allow to incorporate prior
knowledge about the shape that is to be reconstructed. To this end, we propose
the use of a statistical shape model (SSM) as a prior for surface
reconstruction. The SSM is represented by a point distribution model (PDM),
which is associated with a surface mesh. Using the shape distribution that is
modelled by the PDM, we formulate the problem of surface reconstruction from a
probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to
do so, the given points are interpreted as samples of the GMM. By using mixture
components with anisotropic covariances that are "oriented" according to the
surface normals at the PDM points, a surface-based fitting is accomplished.
Estimating the parameters of the GMM in a maximum a posteriori manner yields
the reconstruction of the surface from the given data points. We compare our
method to the extensively used Iterative Closest Points method on several
different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and
demonstrate superior accuracy and robustness on sparse data.
Theorem Proving Based on Semantics of DNA Strand Graph
Authors: Kumar S. Ray, Mandrita Mondal
Date: 2017-02-15 00:00:00
Tasks: Automated Theorem Proving

Because of several technological limitations of traditional silicon based
computing, for past few years a paradigm shift, from silicon to carbon, is
occurring in computational world. DNA computing has been considered to be quite
promising in solving computational and reasoning problems by using DNA strands.
Resolution, an important aspect of automated theorem proving and mathematical
logic, is a rule of inference which leads to proof by contradiction technique
for sentences in propositional logic and first-order logic. This can also be
called refutation theorem-proving. In this paper we have shown how the theorem
proving with resolution refutation by DNA computation can be represented by the
semantics of process calculus and strand graph.
A Region Based Easy Path Wavelet Transform For Sparse Image Representation
Authors: Renato Budinich
Date: 2017-02-07 00:00:00

The Easy Path Wavelet Transform is an adaptive transform for bivariate
functions (in particular natural images) which has been proposed in [1]. It
provides a sparse representation by finding a path in the domain of the
function leveraging the local correlations of the function values. It then
applies a one dimensional wavelet transform to the obtained vector, decimates
the points and iterates the procedure. The main drawback of such method is the
need to store, for each level of the transform, the path which vectorizes the
two dimensional data. Here we propose a variation on the method which consists
of firstly applying a segmentation procedure to the function domain,
partitioning it into regions where the variation in the function values is low;
in a second step, inside each such region, a path is found in some
deterministic way, i.e. not data-dependent. This circumvents the need to store
the paths at each level, while still obtaining good quality lossy compression.
This method is particularly well suited to encode a Region of Interest in the
image with different quality than the rest of the image.
  [1] Gerlind Plonka. The easy path wavelet transform: A new adaptive wavelet
transform for sparse representation of two-dimensional data. Multiscale
Modeling & Simulation, 7(3):1474$-$1496, 2008.
Frustratingly Short Attention Spans in Neural Language Modeling
Authors: Michał Daniluk, Tim Rocktäschel, Johannes Welbl, Sebastian Riedel
Date: 2017-02-15 00:00:00
Tasks: Language Modeling, Language Modelling

Neural language models predict the next token using a latent representation
of the immediate token history. Recently, various methods for augmenting neural
language models with an attention mechanism over a differentiable memory have
been proposed. For predicting the next token, these models query information
from a memory of the recent history which can facilitate learning mid- and
long-range dependencies. However, conventional attention mechanisms used in
memory-augmented neural language models produce a single output vector per time
step. This vector is used both for predicting the next token as well as for the
key and value of a differentiable memory of a token history. In this paper, we
propose a neural language model with a key-value attention mechanism that
outputs separate representations for the key and value of a differentiable
memory, as well as for encoding the next-word distribution. This model
outperforms existing memory-augmented neural language models on two corpora.
Yet, we found that our method mainly utilizes a memory of the five most recent
output representations. This led to the unexpected main finding that a much
simpler model based only on the concatenation of recent output representations
from previous time steps is on par with more sophisticated memory-augmented
neural language models.
A Dependency-Based Neural Reordering Model for Statistical Machine Translation
Authors: Christian Hadiwinoto, Hwee Tou Ng
Date: 2017-02-15 00:00:00
Tasks: Machine Translation, Sentence, Translation

In machine translation (MT) that involves translating between two languages
with significant differences in word order, determining the correct word order
of translated words is a major challenge. The dependency parse tree of a source
sentence can help to determine the correct word order of the translated words.
In this paper, we present a novel reordering approach utilizing a neural
network and dependency-based embeddings to predict whether the translations of
two source words linked by a dependency relation should remain in the same
order or should be swapped in the translated sentence. Experiments on
Chinese-to-English translation show that our approach yields a statistically
significant improvement of 0.57 BLEU point on benchmark NIST test sets,
compared to our prior state-of-the-art statistical MT system that uses sparse
dependency-based reordering features.
IDSA: Intelligent Distributed Sensor Activation Algorithm For Target Tracking With Wireless Sensor Network
Authors: Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hoseini
Date: 2015-05-30 00:00:00

One important application of the Wireless Sensor Network(WSN) is target
tracking, the aim of this application is converging to an event or object in an
area. In this paper, we propose an energy-efficient distributed sensor
activation protocol based on predicted location technique, called Intelligent
Distributed Sensor Activation Algorithm (IDSA). The proposed algorithm predicts
the location of target in the next time interval, by analyzing current location
and movement history of the target, this prediction is done by computational
intelligence. The fewest essential number of sensor nodes within the predicted
location will be activated to cover the target. The results show that the
proposed method outperforms the existing methods such as Na\"ive and DSA in
terms of energy consumption and the number of nodes that was involved in
tracking the target.
Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
Authors: Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, Carlos Castillo, Rama Chellappa
Date: 2017-02-15 00:00:00
Tasks: Face Recognition, Face Verification

Although deep learning has yielded impressive performance for face
recognition, many studies have shown that different networks learn different
feature maps: while some networks are more receptive to pose and illumination
others appear to capture more local information. Thus, in this work, we propose
a deep heterogeneous feature fusion network to exploit the complementary
information present in features generated by different deep convolutional
neural networks (DCNNs) for template-based face recognition, where a template
refers to a set of still face images or video frames from different sources
which introduces more blur, pose, illumination and other variations than
traditional face datasets. The proposed approach efficiently fuses the
discriminative information of different deep features by 1) jointly learning
the non-linear high-dimensional projection of the deep features and 2)
generating a more discriminative template representation which preserves the
inherent geometry of the deep features in the feature space. Experimental
results on the IARPA Janus Challenge Set 3 (Janus CS3) dataset demonstrate that
the proposed method can effectively improve the recognition performance. In
addition, we also present a series of covariate experiments on the face
verification task for in-depth qualitative evaluations for the proposed
approach.
Neural Architecture Search with Reinforcement Learning
Authors: Barret Zoph, Quoc V. Le
Date: 2016-11-05 00:00:00
Tasks: Image Classification, Language Modeling, Language Modelling, Natural Language Understanding, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is
0.09 percent better and 1.05x faster than the previous state-of-the-art model
that used a similar architectural scheme. On the Penn Treebank dataset, our
model can compose a novel recurrent cell that outperforms the widely-used LSTM
cell, and other state-of-the-art baselines. Our cell achieves a test set
perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than
the previous state-of-the-art model. The cell can also be transferred to the
character language modeling task on PTB and achieves a state-of-the-art
perplexity of 1.214.
Telugu OCR Framework using Deep Learning
Authors: Rakesh Achanta, Trevor Hastie
Date: 2015-09-20 00:00:00
Tasks: Deep Learning, General Classification, Language Modeling, Language Modelling, Optical Character Recognition, Optical Character Recognition (OCR)

In this paper, we address the task of Optical Character Recognition(OCR) for
the Telugu script. We present an end-to-end framework that segments the text
image, classifies the characters and extracts lines using a language model. The
segmentation is based on mathematical morphology. The classification module,
which is the most challenging task of the three, is a deep convolutional neural
network. The language is modelled as a third degree markov chain at the glyph
level. Telugu script is a complex alphasyllabary and the language is
agglutinative, making the problem hard. In this paper we apply the latest
advances in neural networks to achieve state-of-the-art error rates. We also
review convolutional neural networks in great detail and expound the
statistical justification behind the many tricks needed to make Deep Learning
work.
Efficient Multitask Feature and Relationship Learning
Authors: Han Zhao, Otilia Stretcu, Alex Smola, Geoff Gordon
Date: 2017-02-14 00:00:00

We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains. In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively. First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization. We then propose an alternative formulation, as well as an efficient algorithm to optimize it. Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors. We also provide a nonlinear extension that is able to achieve better generalization than existing methods.
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
Authors: Feng Mao, Edgar Blanco, Mingang Fu, Rohit Jain, Anurag Gupta, Sebastien Mancel, Rong Yuan, Stephen Guo, Sai Kumar, Yayang Tian
Date: 2017-02-14 00:00:00
Tasks: Feature Engineering, feature selection

Bin Packing problems have been widely studied because of their broad
applications in different domains. Known as a set of NP-hard problems, they
have different vari- ations and many heuristics have been proposed for
obtaining approximate solutions. Specifically, for the 1D variable sized bin
packing problem, the two key sets of optimization heuristics are the bin
assignment and the bin allocation. Usually the performance of a single static
optimization heuristic can not beat that of a dynamic one which is tailored for
each bin packing instance. Building such an adaptive system requires modeling
the relationship between bin features and packing perform profiles. The primary
drawbacks of traditional AI machine learnings for this task are the natural
limitations of feature engineering, such as the curse of dimensionality and
feature selection quality. We introduce a deep learning approach to overcome
the drawbacks by applying a large training data set, auto feature selection and
fast, accurate labeling. We show in this paper how to build such a system by
both theoretical formulation and engineering practices. Our prediction system
achieves up to 89% training accuracy and 72% validation accuracy to select the
best heuristic that can generate a better quality bin packing solution.