pwc-archive/papers-with-abstracts huggingface dataset:
2D Discrete Fourier Transform with Simultaneous Edge Artifact Removal for Real-Time Applications
Authors: Faisal Mahmood, Märt Toots, Lars-Göran Öfverstedt, Ulf Skoglund
Date: 2016-03-16 00:00:00

Two-Dimensional (2D) Discrete Fourier Transform (DFT) is a basic and
computationally intensive algorithm, with a vast variety of applications. 2D
images are, in general, non-periodic, but are assumed to be periodic while
calculating their DFTs. This leads to cross-shaped artifacts in the frequency
domain due to spectral leakage. These artifacts can have critical consequences
if the DFTs are being used for further processing. In this paper we present a
novel FPGA-based design to calculate high-throughput 2D DFTs with simultaneous
edge artifact removal. Standard approaches for removing these artifacts using
apodization functions or mirroring, either involve removing critical
frequencies or a surge in computation by increasing image size. We use a
periodic-plus-smooth decomposition based artifact removal algorithm optimized
for FPGA implementation, while still achieving real-time ($\ge$23 frames per
second) performance for a 512$\times$512 size image stream. Our optimization
approach leads to a significant decrease in external memory utilization thereby
avoiding memory conflicts and simplifies the design. We have tested our design
on a PXIe based Xilinx Kintex 7 FPGA system communicating with a host PC which
gives us the advantage to further expand the design for industrial
applications.
Feature Selection as a Multiagent Coordination Problem
Authors: Kleanthis Malialis, Jun Wang, Gary Brooks, George Frangou
Date: 2016-03-16 00:00:00
Tasks: feature selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Datasets with hundreds to tens of thousands features is the new norm. Feature
selection constitutes a central problem in machine learning, where the aim is
to derive a representative set of features from which to construct a
classification (or prediction) model for a specific task. Our experimental
study involves microarray gene expression datasets, these are high-dimensional
and noisy datasets that contain genetic data typically used for distinguishing
between benign or malicious tissues or classifying different types of cancer.
In this paper, we formulate feature selection as a multiagent coordination
problem and propose a novel feature selection method using multiagent
reinforcement learning. The central idea of the proposed approach is to
"assign" a reinforcement learning agent to each feature where each agent learns
to control a single feature, we refer to this approach as MARL. Applying this
to microarray datasets creates an enormous multiagent coordination problem
between thousands of learning agents. To address the scalability challenge we
apply a form of reward shaping called CLEAN rewards. We compare in total nine
feature selection methods, including state-of-the-art methods, and show that
the proposed method using CLEAN rewards can significantly scale-up, thus
outperforming the rest of learning-based methods. We further show that a hybrid
variant of MARL achieves the best overall performance.
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
Authors: Qiyang Zhao, Lewis D. Griffin
Date: 2016-03-16 00:00:00

Many deep Convolutional Neural Networks (CNN) make incorrect predictions on
adversarial samples obtained by imperceptible perturbations of clean samples.
We hypothesize that this is caused by a failure to suppress unusual signals
within network layers. As remedy we propose the use of Symmetric Activation
Functions (SAF) in non-linear signal transducer units. These units suppress
signals of exceptional magnitude. We prove that SAF networks can perform
classification tasks to arbitrary precision in a simplified situation. In
practice, rather than use SAFs alone, we add them into CNNs to improve their
robustness. The modified CNNs can be easily trained using popular strategies
with the moderate training load. Our experiments on MNIST and CIFAR-10 show
that the modified CNNs perform similarly to plain ones on clean samples, and
are remarkably more robust against adversarial and nonsense samples.
Human Gender Classification: A Review
Authors: Yingxiao Wu, Yan Zhuang, Xi Long, Feng Lin, Wenyao Xu
Date: 2015-07-17 00:00:00
Tasks: Classification, Gender Classification, General Classification

Gender contains a wide range of information regarding to the characteristics
difference between male and female. Successful gender recognition is essential
and critical for many applications in the commercial domains such as
applications of human-computer interaction and computer-aided physiological or
psychological analysis. Some have proposed various approaches for automatic
gender classification using the features derived from human bodies and/or
behaviors. First, this paper introduces the challenge and application for
gender classification research. Then, the development and framework of gender
classification are described. Besides, we compare these state-of-the-art
approaches, including vision-based methods, biological information-based
method, and social network information-based method, to provide a comprehensive
review in the area of gender classification. In mean time, we highlight the
strength and discuss the limitation of each method. Finally, this review also
discusses several promising applications for the future work.
On semidefinite relaxations for the block model
Authors: Arash A. Amini, Elizaveta Levina
Date: 2014-06-21 00:00:00
Tasks: Community Detection, Graphon Estimation, model, Stochastic Block Model

The stochastic block model (SBM) is a popular tool for community detection in
networks, but fitting it by maximum likelihood (MLE) involves a computationally
infeasible optimization problem. We propose a new semidefinite programming
(SDP) solution to the problem of fitting the SBM, derived as a relaxation of
the MLE. We put ours and previously proposed SDPs in a unified framework, as
relaxations of the MLE over various sub-classes of the SBM, revealing a
connection to sparse PCA. Our main relaxation, which we call SDP-1, is tighter
than other recently proposed SDP relaxations, and thus previously established
theoretical guarantees carry over. However, we show that SDP-1 exactly recovers
true communities over a wider class of SBMs than those covered by current
results. In particular, the assumption of strong assortativity of the SBM,
implicit in consistency conditions for previously proposed SDPs, can be relaxed
to weak assortativity for our approach, thus significantly broadening the class
of SBMs covered by the consistency results. We also show that strong
assortativity is indeed a necessary condition for exact recovery for previously
proposed SDP approaches and not an artifact of the proofs. Our analysis of SDPs
is based on primal-dual witness constructions, which provides some insight into
the nature of the solutions of various SDPs. We show how to combine features
from SDP-1 and already available SDPs to achieve the most flexibility in terms
of both assortativity and block-size constraints, as our relaxation has the
tendency to produce communities of similar sizes. This tendency makes it the
ideal tool for fitting network histograms, a method gaining popularity in the
graphon estimation literature, as we illustrate on an example of a social
networks of dolphins. We also provide empirical evidence that SDPs outperform
spectral methods for fitting SBMs with a large number of blocks.
Short-term time series prediction using Hilbert space embeddings of autoregressive processes
Authors: Edgar A. Valencia, Mauricio A. Álvarez
Date: 2016-03-16 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Linear autoregressive models serve as basic representations of discrete time
stochastic processes. Different attempts have been made to provide non-linear
versions of the basic autoregressive process, including different versions
based on kernel methods. Motivated by the powerful framework of Hilbert space
embeddings of distributions, in this paper we apply this methodology for the
kernel embedding of an autoregressive process of order $p$. By doing so, we
provide a non-linear version of an autoregressive process, that shows increased
performance over the linear model in highly complex time series. We use the
method proposed for one-step ahead forecasting of different time-series, and
compare its performance against other non-linear methods.
A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data
Authors: Hussein A. Abbass, George Leu, Kathryn Merrick
Date: 2016-03-16 00:00:00

Despite the advances made in artificial intelligence, software agents, and
robotics, there is little we see today that we can truly call a fully
autonomous system. We conjecture that the main inhibitor for advancing autonomy
is lack of trust. Trusted autonomy is the scientific and engineering field to
establish the foundations and ground work for developing trusted autonomous
systems (robotics and software agents) that can be used in our daily life, and
can be integrated with humans seamlessly, naturally and efficiently.
  In this paper, we review this literature to reveal opportunities for
researchers and practitioners to work on topics that can create a leap forward
in advancing the field of trusted autonomy. We focus the paper on the `trust'
component as the uniting technology between humans and machines. Our inquiry
into this topic revolves around three sub-topics: (1) reviewing and positioning
the trust modelling literature for the purpose of trusted autonomy; (2)
reviewing a critical subset of sensor technologies that allow a machine to
sense human states; and (3) distilling some critical questions for advancing
the field of trusted autonomy. The inquiry is augmented with conceptual models
that we propose along the way by recompiling and reshaping the literature into
forms that enables trusted autonomous systems to become a reality. The paper
offers a vision for a Trusted Cyborg Swarm, an extension of our previous
Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in
a harmonious, seamless, and coordinated manner.
Non-linear Dimensionality Regularizer for Solving Inverse Problems
Authors: Ravi Garg, Anders Eriksson, Ian Reid
Date: 2016-03-16 00:00:00

Consider an ill-posed inverse problem of estimating causal factors from
observations, one of which is known to lie near some (un- known)
low-dimensional, non-linear manifold expressed by a predefined Mercer-kernel.
Solving this problem requires simultaneous estimation of these factors and
learning the low-dimensional representation for them. In this work, we
introduce a novel non-linear dimensionality regulariza- tion technique for
solving such problems without pre-training. We re-formulate Kernel-PCA as an
energy minimization problem in which low dimensionality constraints are
introduced as regularization terms in the energy. To the best of our knowledge,
ours is the first at- tempt to create a dimensionality regularizer in the KPCA
framework. Our approach relies on robustly penalizing the rank of the recovered
fac- tors directly in the implicit feature space to create their
low-dimensional approximations in closed form. Our approach performs robust
KPCA in the presence of missing data and noise. We demonstrate state-of-the-art
results on predicting missing entries in the standard oil flow dataset.
Additionally, we evaluate our method on the challenging problem of Non-Rigid
Structure from Motion and our approach delivers promising results on CMU mocap
dataset despite the presence of significant occlusions and noise.
Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing
Authors: Gary Pui-Tung Choi, Kin Tat Ho, Lok Ming Lui
Date: 2015-08-30 00:00:00

Point cloud is the most fundamental representation of 3D geometric objects.
Analyzing and processing point cloud surfaces is important in computer graphics
and computer vision. However, most of the existing algorithms for surface
analysis require connectivity information. Therefore, it is desirable to
develop a mesh structure on point clouds. This task can be simplified with the
aid of a parameterization. In particular, conformal parameterizations are
advantageous in preserving the geometric information of the point cloud data.
In this paper, we extend a state-of-the-art spherical conformal
parameterization algorithm for genus-0 closed meshes to the case of point
clouds, using an improved approximation of the Laplace-Beltrami operator on
data points. Then, we propose an iterative scheme called the North-South
reiteration for achieving a spherical conformal parameterization. A balancing
scheme is introduced to enhance the distribution of the spherical
parameterization. High quality triangulations and quadrangulations can then be
built on the point clouds with the aid of the parameterizations. Also, the
meshes generated are guaranteed to be genus-0 closed meshes. Moreover, using
our proposed spherical conformal parameterization, multilevel representations
of point clouds can be easily constructed. Experimental results demonstrate the
effectiveness of our proposed framework.
Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems
Authors: Aryan Mokhtari, Shahin Shahrampour, Ali Jadbabaie, Alejandro Ribeiro
Date: 2016-03-16 00:00:00

In this paper, we address tracking of a time-varying parameter with unknown
dynamics. We formalize the problem as an instance of online optimization in a
dynamic setting. Using online gradient descent, we propose a method that
sequentially predicts the value of the parameter and in turn suffers a loss.
The objective is to minimize the accumulation of losses over the time horizon,
a notion that is termed dynamic regret. While existing methods focus on convex
loss functions, we consider strongly convex functions so as to provide better
guarantees of performance. We derive a regret bound that captures the
path-length of the time-varying parameter, defined in terms of the distance
between its consecutive values. In other words, the bound represents the
natural connection of tracking quality to the rate of change of the parameter.
We provide numerical experiments to complement our theoretical findings.
Audio Classical Composer Identification by Deep Neural Network
Authors: Zhen Hu, Kun fu, Chang-Shui Zhang
Date: 2013-01-15 00:00:00
Tasks: Denoising, Information Retrieval, Music Information Retrieval, Retrieval

Audio Classical Composer Identification (ACC) is an important problem in
Music Information Retrieval (MIR) which aims at identifying the composer for
audio classical music clips. The famous annual competition, Music Information
Retrieval Evaluation eXchange (MIREX), also takes it as one of the four
training&testing tasks. We built a hybrid model based on Deep Belief Network
(DBN) and Stacked Denoising Autoencoder (SDA) to identify the composer from
audio signal. As a matter of copyright, sponsors of MIREX cannot publish their
data set. We built a comparable data set to test our model. We got an accuracy
of 76.26% in our data set which is better than some pure models and shallow
models. We think our method is promising even though we test it in a different
data set, since our data set is comparable to that in MIREX by size. We also
found that samples from different classes become farther away from each other
when transformed by more layers in our model.
On the Complexity of One-class SVM for Multiple Instance Learning
Authors: Zhen Hu, Zhuyin Xue
Date: 2016-03-16 00:00:00
Tasks: Multiple Instance Learning

In traditional multiple instance learning (MIL), both positive and negative
bags are required to learn a prediction function. However, a high human cost is
needed to know the label of each bag---positive or negative. Only positive bags
contain our focus (positive instances) while negative bags consist of noise or
background (negative instances). So we do not expect to spend too much to label
the negative bags. Contrary to our expectation, nearly all existing MIL methods
require enough negative bags besides positive ones. In this paper we propose an
algorithm called "Positive Multiple Instance" (PMI), which learns a classifier
given only a set of positive bags. So the annotation of negative bags becomes
unnecessary in our method. PMI is constructed based on the assumption that the
unknown positive instances in positive bags be similar each other and
constitute one compact cluster in feature space and the negative instances
locate outside this cluster. The experimental results demonstrate that PMI
achieves the performances close to or a little worse than those of the
traditional MIL algorithms on benchmark and real data sets. However, the number
of training bags in PMI is reduced significantly compared with traditional MIL
algorithms.
Data Clustering and Graph Partitioning via Simulated Mixing
Authors: Shahzad Bhatti, Carolyn Beck, Angelia Nedic
Date: 2016-03-15 00:00:00
Tasks: Clustering, graph partitioning

Spectral clustering approaches have led to well-accepted algorithms for
finding accurate clusters in a given dataset. However, their application to
large-scale datasets has been hindered by computational complexity of
eigenvalue decompositions. Several algorithms have been proposed in the recent
past to accelerate spectral clustering, however they compromise on the accuracy
of the spectral clustering to achieve faster speed. In this paper, we propose a
novel spectral clustering algorithm based on a mixing process on a graph.
Unlike the existing spectral clustering algorithms, our algorithm does not
require computing eigenvectors. Specifically, it finds the equivalent of a
linear combination of eigenvectors of the normalized similarity matrix weighted
with corresponding eigenvalues. This linear combination is then used to
partition the dataset into meaningful clusters. Simulations on real datasets
show that partitioning datasets based on such linear combinations of
eigenvectors achieves better accuracy than standard spectral clustering methods
as the number of clusters increase. Our algorithm can easily be implemented in
a distributed setting.
Bias Correction for Regularized Regression and its Application in Learning with Streaming Data
Authors: Qiang Wu
Date: 2016-03-15 00:00:00
Tasks: Incremental Learning, regression

We propose an approach to reduce the bias of ridge regression and
regularization kernel network. When applied to a single data set the new
algorithms have comparable learning performance with the original ones. When
applied to incremental learning with block wise streaming data the new
algorithms are more efficient due to bias reduction. Both theoretical
characterizations and simulation studies are used to verify the effectiveness
of these new algorithms.
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
Authors: Zhicheng Yan, Hao Zhang, Yangqing Jia, Thomas Breuel, Yizhou Yu
Date: 2016-03-15 00:00:00
Tasks: Semantic Segmentation

State-of-the-art results of semantic segmentation are established by Fully
Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and
pooling layers to gradually enlarge the receptive fields of neurons, resulting
in an indirect way of modeling the distant contextual dependence. In this work,
we advocate the use of spatially recurrent layers (i.e. ReNet layers) which
directly capture global contexts and lead to improved feature representations.
We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet
(N-ReNet), which achieves competitive performance on Stanford Background
dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel
Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including
full-image receptive fields, end-to-end training, and efficient network
execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results
of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%,
2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20
object classes.
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Authors: Debapriya Maji, Anirban Santara, Pabitra Mitra, Debdoot Sheet
Date: 2016-03-15 00:00:00
Tasks: Ensemble Learning, Vessel Detection

Vision impairment due to pathological damage of the retina can largely be
prevented through periodic screening using fundus color imaging. However the
challenge with large scale screening is the inability to exhaustively detect
fine blood vessels crucial to disease diagnosis. In this work we present a
computational imaging framework using deep and ensemble learning for reliable
detection of blood vessels in fundus color images. An ensemble of deep
convolutional neural networks is trained to segment vessel and non-vessel areas
of a color fundus image. During inference, the responses of the individual
ConvNets of the ensemble are averaged to form the final segmentation. In
experimental evaluation with the DRIVE database, we achieve the objective of
vessel detection with maximum average accuracy of 94.7\% and area under ROC
curve of 0.9283.
An optimal algorithm for bandit convex optimization
Authors: Elad Hazan, Yuanzhi Li
Date: 2016-03-14 00:00:00

We consider the problem of online convex optimization against an arbitrary
adversary with bandit feedback, known as bandit convex optimization. We give
the first $\tilde{O}(\sqrt{T})$-regret algorithm for this setting based on a
novel application of the ellipsoid method to online learning. This bound is
known to be tight up to logarithmic factors. Our analysis introduces new tools
in discrete convex geometry.
Evaluating the word-expert approach for Named-Entity Disambiguation
Authors: Angel X. Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre
Date: 2016-03-15 00:00:00
Tasks: Entity Disambiguation, Word Sense Disambiguation

Named Entity Disambiguation (NED) is the task of linking a named-entity
mention to an instance in a knowledge-base, typically Wikipedia. This task is
closely related to word-sense disambiguation (WSD), where the supervised
word-expert approach has prevailed. In this work we present the results of the
word-expert approach to NED, where one classifier is built for each target
entity mention string. The resources necessary to build the system, a
dictionary and a set of training instances, have been automatically derived
from Wikipedia. We provide empirical evidence of the value of this approach, as
well as a study of the differences between WSD and NED, including ambiguity and
synonymy statistics.
Topic Modeling Using Distributed Word Embeddings
Authors: Ramandeep S Randhawa, Parag Jain, Gagan Madan
Date: 2016-03-15 00:00:00
Tasks: Word Embeddings

We propose a new algorithm for topic modeling, Vec2Topic, that identifies the
main topics in a corpus using semantic information captured via
high-dimensional distributed word embeddings. Our technique is unsupervised and
generates a list of topics ranked with respect to importance. We find that it
works better than existing topic modeling techniques such as Latent Dirichlet
Allocation for identifying key topics in user-generated content, such as
emails, chats, etc., where topics are diffused across the corpus. We also find
that Vec2Topic works equally well for non-user generated content, such as
papers, reports, etc., and for small corpora such as a single-document.