pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Techniques for Gridding cDNA Microarray Images
Authors: Naima Kaabouch, Hamid Shahbazkia
Date: 2016-07-03 00:00:00
Tasks: Template Matching

Microarray is considered an important instrument and powerful new technology
for large-scale gene sequence and gene expression analysis. One of the major
challenges of this technique is the image processing phase. The accuracy of
this phase has an important impact on the accuracy and effectiveness of the
subsequent gene expression and identification analysis. The processing can be
organized mainly into four steps: gridding, spot isolation, segmentation, and
quantification. Although several commercial software packages are now
available, microarray image analysis still requires some intervention by the
user, and thus a certain level of image processing expertise. This paper
describes and compares four techniques that perform automatic gridding and spot
isolation. The proposed techniques are based on template matching technique,
standard deviation, sum, and derivative of these profiles. Experimental results
show that the accuracy of the derivative of the sum profile is highly accurate
compared to other techniques for good and poor quality microarray images.
An Analysis System for DNA Gel Electrophoresis Images Based on Automatic Thresholding an Enhancement
Authors: Naima Kaabouch, Richard R. Schultz, Barry Milavetz, Lata Balakrishnan
Date: 2016-07-03 00:00:00

Gel electrophoresis, a widely used technique to separate DNA according to
their size and weight, generates images that can be analyzed automatically.
Manual or semiautomatic image processing presents a bottleneck for further
development and leads to reproducibility issues. In this paper, we present a
fully automated system with high accuracy for analyzing DNA and proteins. The
proposed algorithm consists of four main steps: automatic thresholding,
shifting, filtering, and data processing. Automatic thresholding, used to
equalize the gray values of the gel electrophoresis image background, is one of
the novel operations in this algorithm. Enhancement is also used to improve
poor quality images that have faint DNA bands. Experimental results show that
the proposed technique eliminates defects due to noise for average quality gel
electrophoresis images, while it also improves the quality of poor images.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Authors: Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng
Date: 2016-07-03 00:00:00
Tasks: Liver Segmentation, Segmentation

Automatic liver segmentation from CT volumes is a crucial prerequisite yet
challenging task for computer-aided hepatic disease diagnosis and treatment. In
this paper, we present a novel 3D deeply supervised network (3D DSN) to address
this challenging task. The proposed 3D DSN takes advantage of a fully
convolutional architecture which performs efficient end-to-end learning and
inference. More importantly, we introduce a deep supervision mechanism during
the learning process to combat potential optimization difficulties, and thus
the model can acquire a much faster convergence rate and more powerful
discrimination capability. On top of the high-quality score map produced by the
3D DSN, a conditional random field model is further employed to obtain refined
segmentation results. We evaluated our framework on the public MICCAI-SLiver07
dataset. Extensive experiments demonstrated that our method achieves
competitive segmentation results to state-of-the-art approaches with a much
faster processing speed.
Context-Dependent Word Representation for Neural Machine Translation
Authors: Heeyoul Choi, Kyunghyun Cho, Yoshua Bengio
Date: 2016-07-03 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation

We first observe a potential weakness of continuous vector representations of
symbols in neural machine translation. That is, the continuous vector
representation, or a word embedding vector, of a symbol encodes multiple
dimensions of similarity, equivalent to encoding more than one meaning of the
word. This has the consequence that the encoder and decoder recurrent networks
in neural machine translation need to spend substantial amount of their
capacity in disambiguating source and target words based on the context which
is defined by a source sentence. Based on this observation, in this paper we
propose to contextualize the word embedding vectors using a nonlinear
bag-of-words representation of the source sentence. Additionally, we propose to
represent special tokens (such as numbers, proper nouns and acronyms) with
typed symbols to facilitate translating those words that are not well-suited to
be translated via continuous vectors. The experiments on En-Fr and En-De reveal
that the proposed approaches of contextualization and symbolization improves
the translation quality of neural machine translation systems significantly.
A Hierarchical Distributed Processing Framework for Big Image Data
Authors: Le Dong, Zhiyu Lin, Yan Liang, Ling He, Ning Zhang, Qi Chen, Xiaochun Cao, Ebroul lzquierdo
Date: 2016-07-03 00:00:00

This paper introduces an effective processing framework nominated ICP (Image
Cloud Processing) to powerfully cope with the data explosion in image
processing field. While most previous researches focus on optimizing the image
processing algorithms to gain higher efficiency, our work dedicates to
providing a general framework for those image processing algorithms, which can
be implemented in parallel so as to achieve a boost in time efficiency without
compromising the results performance along with the increasing image scale. The
proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and
DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image
data pre-stored in the distributed system, while DICP is proposed for dynamic
input. To accomplish SICP, two novel data representations named P-Image and
Big-Image are designed to cooperate with MapReduce to achieve more optimized
configuration and higher efficiency. DICP is implemented through a parallel
processing procedure working with the traditional processing mechanism of the
distributed system. Representative results of comprehensive experiments on the
challenging ImageNet dataset are selected to validate the capacity of our
proposed ICP framework over the traditional state-of-the-art methods, both in
time efficiency and quality of results.
Graphical Exponential Screening
Authors: Zhe Liu
Date: 2015-03-09 00:00:00

In high dimensions we propose and analyze an aggregation estimator of the
precision matrix for Gaussian graphical models. This estimator, called
graphical Exponential Screening (gES), linearly combines a suitable set of
individual estimators with different underlying graphs, and balances the
estimation error and sparsity. We study the risk of this aggregation estimator
and show that it is comparable to that of the best estimator based on a single
graph, chosen by an oracle. Numerical performance of our method is investigated
using both simulated and real datasets, in comparison with some state-of-art
estimation procedures.
Representation learning for very short texts using weighted word embedding aggregation
Authors: Cedric De Boom, Steven Van Canneyt, Thomas Demeester, Bart Dhoedt
Date: 2016-07-02 00:00:00
Tasks: Event Detection, News Recommendation, Opinion Mining, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Short text messages such as tweets are very noisy and sparse in their use of
vocabulary. Traditional textual representations, such as tf-idf, have
difficulty grasping the semantic meaning of such texts, which is important in
applications such as event detection, opinion mining, news recommendation, etc.
We constructed a method based on semantic word embeddings and frequency
information to arrive at low-dimensional representations for short texts
designed to capture semantic similarity. For this purpose we designed a
weight-based model and a learning procedure based on a novel median-based loss
function. This paper discusses the details of our model and the optimization
methods, together with the experimental results on both Wikipedia and Twitter
data. We find that our method outperforms the baseline approaches in the
experiments, and that it generalizes well on different word embeddings without
retraining. Our method is therefore capable of retaining most of the semantic
information in the text, and is applicable out-of-the-box.
Tight Risk Bounds for Multi-Class Margin Classifiers
Authors: Yury Maximov, Daria Reshetova
Date: 2015-07-10 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

We consider a problem of risk estimation for large-margin multi-class
classifiers. We propose a novel risk bound for the multi-class classification
problem. The bound involves the marginal distribution of the classifier and the
Rademacher complexity of the hypothesis class. We prove that our bound is tight
in the number of classes. Finally, we compare our bound with the related ones
and provide a simplified version of the bound for the multi-class
classification with kernel based hypotheses.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
Authors: Ehsan Hosseini-Asl, Georgy Gimel'farb, Ayman El-Baz
Date: 2016-07-02 00:00:00
Tasks: General Classification, Hippocampus, Skull Stripping

Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer's disease (AD), is based on classification of features
extracted from brain images. The features have to accurately capture main
AD-related variations of anatomical brain structures, such as, e.g., ventricles
size, hippocampus shape, cortical thickness, and brain volume. This paper
proposes to predict the AD with a deep 3D convolutional neural network
(3D-CNN), which can learn generic features capturing AD biomarkers and adapt to
different domain datasets. The 3D-CNN is built upon a 3D convolutional
autoencoder, which is pre-trained to capture anatomical shape variations in
structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then
fine-tuned for each task-specific AD classification. Experiments on the
\emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our
3D-CNN outperforms several conventional classifiers by accuracy and robustness.
Abilities of the 3D-CNN to generalize the features learnt and adapt to other
domains have been validated on the \emph{CADDementia} dataset.
Active Object Localization in Visual Situations
Authors: Max H. Quinn, Anthony D. Rhodes, Melanie Mitchell
Date: 2016-07-02 00:00:00
Tasks: Active Object Localization, Object, Object Localization

We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.
Text comparison using word vector representations and dimensionality reduction
Authors: Hendrik Heuer
Date: 2016-07-02 00:00:00
Tasks: Dimensionality Reduction

This paper describes a technique to compare large text sources using word
vector representations (word2vec) and dimensionality reduction (t-SNE) and how
it can be implemented using Python. The technique provides a bird's-eye view of
text sources, e.g. text summaries and their source material, and enables users
to explore text sources like a geographical map. Word vector representations
capture many linguistic properties such as gender, tense, plurality and even
semantic concepts like "capital city of". Using dimensionality reduction, a 2D
map can be computed where semantically similar words are close to each other.
The technique uses the word2vec model from the gensim Python library and t-SNE
from scikit-learn.
Approximate Joint Matrix Triangularization
Authors: Nicolo Colombo, Nikos Vlassis
Date: 2016-07-02 00:00:00
Tasks: Tensor Decomposition

We consider the problem of approximate joint triangularization of a set of
noisy jointly diagonalizable real matrices. Approximate joint triangularizers
are commonly used in the estimation of the joint eigenstructure of a set of
matrices, with applications in signal processing, linear algebra, and tensor
decomposition. By assuming the input matrices to be perturbations of
noise-free, simultaneously diagonalizable ground-truth matrices, the
approximate joint triangularizers are expected to be perturbations of the exact
joint triangularizers of the ground-truth matrices. We provide a priori and a
posteriori perturbation bounds on the `distance' between an approximate joint
triangularizer and its exact counterpart. The a priori bounds are theoretical
inequalities that involve functions of the ground-truth matrices and noise
matrices, whereas the a posteriori bounds are given in terms of observable
quantities that can be computed from the input matrices. From a practical
perspective, the problem of finding the best approximate joint triangularizer
of a set of noisy matrices amounts to solving a nonconvex optimization problem.
We show that, under a condition on the noise level of the input matrices, it is
possible to find a good initial triangularizer such that the solution obtained
by any local descent-type algorithm has certain global guarantees. Finally, we
discuss the application of approximate joint matrix triangularization to
canonical tensor decomposition and we derive novel estimation error bounds.
Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications
Authors: Evangelos Psomakelis, Fotis Aisopos, Antonios Litke, Konstantinos Tserpes, Magdalini Kardara, Pablo Martínez Campo
Date: 2016-07-02 00:00:00
Tasks: Retrieval, Sentiment Analysis

In this paper we present a SOA (Service Oriented Architecture)-based
platform, enabling the retrieval and analysis of big datasets stemming from
social networking (SN) sites and Internet of Things (IoT) devices, collected by
smart city applications and socially-aware data aggregation services. A large
set of city applications in the areas of Participating Urbanism, Augmented
Reality and Sound-Mapping throughout participating cities is being applied,
resulting into produced sets of millions of user-generated events and online SN
reports fed into the RADICAL platform. Moreover, we study the application of
data analytics such as sentiment analysis to the combined IoT and SN data saved
into an SQL database, further investigating algorithmic and configurations to
minimize delays in dataset processing and results retrieval.
A Distributed Deep Representation Learning Model for Big Image Data Classification
Authors: Le Dong, Na Lv, Qianni Zhang, Shanshan Xie, Ling He, Mengdie Mao
Date: 2016-07-02 00:00:00
Tasks: Distributed Computing, General Classification, image-classification, Image Classification, Representation Learning

This paper describes an effective and efficient image classification
framework nominated distributed deep representation learning model (DDRL). The
aim is to strike the balance between the computational intensive deep learning
approaches (tuned parameters) which are intended for distributed computing, and
the approaches that focused on the designed parameters but often limited by
sequential computing and cannot scale up. In the evaluation of our approach, it
is shown that DDRL is able to achieve state-of-art classification accuracy
efficiently on both medium and large datasets. The result implies that our
approach is more efficient than the conventional deep learning approaches, and
can be applied to big data that is too complex for parameter designing focused
approaches. More specifically, DDRL contains two main components, i.e., feature
extraction and selection. A hierarchical distributed deep representation
learning algorithm is designed to extract image statistics and a nonlinear
mapping algorithm is used to map the inherent statistics into abstract
features. Both algorithms are carefully designed to avoid millions of
parameters tuning. This leads to a more compact solution for image
classification of big data. We note that the proposed approach is designed to
be friendly with parallel computing. It is generic and easy to be deployed to
different distributed computing resources. In the experiments, the largescale
image datasets are classified with a DDRM implementation on Hadoop MapReduce,
which shows high scalability and resilience.
Siamese convolutional networks based on phonetic features for cognate identification
Authors: Taraka Rama
Date: 2016-05-17 00:00:00

In this paper, we explore the use of convolutional networks (ConvNets) for
the purpose of cognate identification. We compare our architecture with binary
classifiers based on string similarity measures on different language families.
Our experiments show that convolutional networks achieve competitive results
across concepts and across language families at the task of cognate
identification.
Double-detector for Sparse Signal Detection from One Bit Compressed Sensing Measurements
Authors: Hadi Zayyani, Farzan Haddadi, Mehdi Korki
Date: 2016-07-02 00:00:00
Tasks: compressed sensing

This letter presents the sparse vector signal detection from one bit
compressed sensing measurements, in contrast to the previous works which deal
with scalar signal detection. In this letter, available results are extended to
the vector case and the GLRT detector and the optimal quantizer design are
obtained. Also, a double-detector scheme is introduced in which a sensor level
threshold detector is integrated into network level GLRT to improve the
performance. The detection criteria of oracle and clairvoyant detectors are
also derived. Simulation results show that with careful design of the threshold
detector, the overall detection performance of double-detector scheme would be
better than the sign-GLRT proposed in [1] and close to oracle and clairvoyant
detectors. Also, the proposed detector is applied to spectrum sensing and the
results are near the well known energy detector which uses the real valued data
while the proposed detector only uses the sign of the data.
Group Sparse Regularization for Deep Neural Networks
Authors: Simone Scardapane, Danilo Comminiello, Amir Hussain, Aurelio Uncini
Date: 2016-07-02 00:00:00
Tasks: feature selection, Handwritten Digit Recognition

In this paper, we consider the joint task of simultaneously optimizing (i)
the weights of a deep neural network, (ii) the number of neurons for each
hidden layer, and (iii) the subset of active input features (i.e., feature
selection). While these problems are generally dealt with separately, we
present a simple regularized formulation allowing to solve all three of them in
parallel, using standard optimization routines. Specifically, we extend the
group Lasso penalty (originated in the linear regression literature) in order
to impose group-level sparsity on the network's connections, where each group
is defined as the set of outgoing weights from a unit. Depending on the
specific case, the weights can be related to an input variable, to a hidden
neuron, or to a bias unit, thus performing simultaneously all the
aforementioned tasks in order to obtain a compact network. We perform an
extensive experimental evaluation, by comparing with classical weight decay and
Lasso penalties. We show that a sparse version of the group Lasso penalty is
able to achieve competitive performances, while at the same time resulting in
extremely compact networks with a smaller number of input features. We evaluate
both on a toy dataset for handwritten digit recognition, and on multiple
realistic large-scale classification problems.
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
Authors: Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor
Date: 2016-07-02 00:00:00
Tasks: graph construction, Link Prediction

A neighborhood graph, which represents the instances as vertices and their
relations as weighted edges, is the basis of many semi-supervised and
relational models for node labeling and link prediction. Most methods employ a
sequential process to construct the neighborhood graph. This process often
consists of generating a candidate graph, pruning the candidate graph to make a
neighborhood graph, and then performing inference on the variables (i.e.,
nodes) in the neighborhood graph. In this paper, we propose a framework that
can dynamically adapt the neighborhood graph based on the states of variables
from intermediate inference results, as well as structural properties of the
relations connecting them. A key strength of our framework is its ability to
handle multi-relational data and employ varying amounts of relations for each
instance based on the intermediate inference results. We formulate the link
prediction task as inference on neighborhood graphs, and include preliminary
results illustrating the effects of different strategies in our proposed
framework.
Outlier absorbing based on a Bayesian approach
Authors: Parsa Bagherzadeh, Hadi Sadoghi Yazdi
Date: 2016-07-02 00:00:00
Tasks: BIG-bench Machine Learning

The presence of outliers is prevalent in machine learning applications and
may produce misleading results. In this paper a new method for dealing with
outliers and anomal samples is proposed. To overcome the outlier issue, the
proposed method combines the global and local views of the samples. By
combination of these views, our algorithm performs in a robust manner. The
experimental results show the capabilities of the proposed method.
NIST: An Image Classification Network to Image Semantic Retrieval
Authors: Le Dong, Xiuyuan Chen, Mengdie Mao, Qianni Zhang
Date: 2016-07-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval, Semantic Retrieval

This paper proposes a classification network to image semantic retrieval
(NIST) framework to counter the image retrieval challenge. Our approach
leverages the successful classification network GoogleNet based on
Convolutional Neural Networks to obtain the semantic feature matrix which
contains the serial number of classes and corresponding probabilities. Compared
with traditional image retrieval using feature matching to compute the
similarity between two images, NIST leverages the semantic information to
construct semantic feature matrix and uses the semantic distance algorithm to
compute the similarity. Besides, the fusion strategy can significantly reduce
storage and time consumption due to less classes participating in the last
semantic distance computation. Experiments demonstrate that our NIST framework
produces state-of-the-art results in retrieval experiments on MIRFLICKR-25K
dataset.