pwc-archive/papers-with-abstracts huggingface dataset:
Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States
Authors: Aibek Ryskaliyev, Sanzhar Askaruly, Alex Pappachen James
Date: 2016-08-12 00:00:00

A non-invasive method for the monitoring of heart activity can help to reduce
the deaths caused by heart disorders such as stroke, arrhythmia and heart
attack. The human voice can be considered as a biometric data that can be used
for estimation of heart rate. In this paper, we propose a method for estimating
the heart rate from human speech dynamically using voice signal analysis and by
the development of an empirical linear predictor model. The correlation between
the voice signal and heart rate are established by classifiers and prediction
of the heart rates with or without emotions are done using linear models. The
prediction accuracy was tested using the data collected from 15 subjects, it is
about 4050 samples of speech signals and corresponding electrocardiogram
samples. The proposed approach can use for early non-invasive detection of
heart rate changes that can be correlated to an emotional state of the
individual and also can be used as a tool for diagnosis of heart conditions in
real-time situations.
An efficient iterative thresholding method for image segmentation
Authors: Dong Wang, Haohan Li, Xiaoyu Wei, Xiao-Ping Wang
Date: 2016-08-04 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We proposed an efficient iterative thresholding method for multi-phase image
segmentation. The algorithm is based on minimizing piecewise constant
Mumford-Shah functional in which the contour length (or perimeter) is
approximated by a non-local multi-phase energy. The minimization problem is
solved by an iterative method. Each iteration consists of computing simple
convolutions followed by a thresholding step. The algorithm is easy to
implement and has the optimal complexity $O(N \log N)$ per iteration. We also
show that the iterative algorithm has the total energy decaying property. We
present some numerical results to show the efficiency of our method.
Density Matching Reward Learning
Authors: Sungjoon Choi, Kyungjae Lee, Andy Park, Songhwai Oh
Date: 2016-08-12 00:00:00
Tasks: Autonomous Navigation, Reinforcement Learning

In this paper, we focus on the problem of inferring the underlying reward
function of an expert given demonstrations, which is often referred to as
inverse reinforcement learning (IRL). In particular, we propose a model-free
density-based IRL algorithm, named density matching reward learning (DMRL),
which does not require model dynamics. The performance of DMRL is analyzed
theoretically and the sample complexity is derived. Furthermore, the proposed
DMRL is extended to handle nonlinear IRL problems by assuming that the reward
function is in the reproducing kernel Hilbert space (RKHS) and kernel DMRL
(KDMRL) is proposed. The parameters for KDMRL can be computed analytically,
which greatly reduces the computation time. The performance of KDMRL is
extensively evaluated in two sets of experiments: grid world and track driving
experiments. In grid world experiments, the proposed KDMRL method is compared
with both model-based and model-free IRL methods and shows superior performance
on a nonlinear reward setting and competitive performance on a linear reward
setting in terms of expected value differences. Then we move on to more
realistic experiments of learning different driving styles for autonomous
navigation in complex and dynamic tracks using KDMRL and receding horizon
control.
UTSig: A Persian Offline Signature Dataset
Authors: Amir Soleimani, Kazim Fouladi, Babak N. Araabi
Date: 2016-03-10 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The pivotal role of datasets in signature verification systems motivates
researchers to collect signature samples. Distinct characteristics of Persian
signature demands for richer and culture-dependent offline signature datasets.
This paper introduces a new and public Persian offline signature dataset,
UTSig, that consists of 8280 images from 115 classes. Each class has 27 genuine
signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6
forgers. Compared with the other public datasets, UTSig has more samples, more
classes, and more forgers. We considered various variables including signing
period, writing instrument, signature box size, and number of observable
samples for forgers in the data collection procedure. By careful examination of
main characteristics of offline signature datasets, we observe that Persian
signatures have fewer numbers of branch points and end points. We propose and
evaluate four different training and test setups for UTSig. Results of our
experiments show that training genuine samples along with opposite-hand samples
and random forgeries can improve the performance in terms of equal error rate
and minimum cost of log likelihood ratio.
Inferring unknown biological function by integration of GO annotations and gene expression data
Authors: Guillermo Leale, Ariel Bayá, Diego Milone, Pablo Granitto, Georgina Stegmayer
Date: 2016-08-12 00:00:00
Tasks: Clustering

Characterizing genes with semantic information is an important process
regarding the description of gene products. In spite that complete genomes of
many organisms have been already sequenced, the biological functions of all of
their genes are still unknown. Since experimentally studying the functions of
those genes, one by one, would be unfeasible, new computational methods for
gene functions inference are needed. We present here a novel computational
approach for inferring biological function for a set of genes with previously
unknown function, given a set of genes with well-known information. This
approach is based on the premise that genes with similar behaviour should be
grouped together. This is known as the guilt-by-association principle. Thus, it
is possible to take advantage of clustering techniques to obtain groups of
unknown genes that are co-clustered with genes that have well-known semantic
information (GO annotations). Meaningful knowledge to infer unknown semantic
information can therefore be provided by these well-known genes. We provide a
method to explore the potential function of new genes according to those
currently annotated. The results obtained indicate that the proposed approach
could be a useful and effective tool when used by biologists to guide the
inference of biological functions for recently discovered genes. Our work sets
an important landmark in the field of identifying unknown gene functions
through clustering, using an external source of biological input. A simple web
interface to this proposal can be found at
http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.
Reasoning and Algorithm Selection Augmented Symbolic Segmentation
Authors: Martin Lukac, Kamila Abdiyeva, Michitaka Kameyama
Date: 2016-08-12 00:00:00
Tasks: Attribute, Segmentation, Semantic Segmentation

In this paper we present an alternative method to symbolic segmentation: we
approach symbolic segmentation as an algorithm selection problem. That is, let
there be a set A of available algorithms for symbolic segmentation, a set of
input features $F$, a set of image attribute $\mathbb{A}$ and a selection
mechanism $S(F,\mathbb{A},A)$ that selects on a case by case basis the best
algorithm. The semantic segmentation is then an optimization process that
combines best component segments from multiple results into a single optimal
result. The experiments compare three different algorithm selection mechanisms
using three selected semantic segmentation algorithms. The results show that
using the current state of art algorithms and relatively low accuracy of
algorithm selection the accuracy of the semantic segmentation can be improved
by 2\%.
MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos
Authors: Amir Zadeh, Rowan Zellers, Eli Pincus, Louis-Philippe Morency
Date: 2016-06-20 00:00:00
Tasks: Sentiment Analysis, Subjectivity Analysis

People are sharing their opinions, stories and reviews through online video
sharing websites every day. Studying sentiment and subjectivity in these
opinion videos is experiencing a growing attention from academia and industry.
While sentiment analysis has been successful for text, it is an understudied
research question for videos and multimedia content. The biggest setbacks for
studies in this direction are lack of a proper dataset, methodology, baselines
and statistical analysis of how information from different modality sources
relate to each other. This paper introduces to the scientific community the
first opinion-level annotated corpus of sentiment and subjectivity analysis in
online videos called Multimodal Opinion-level Sentiment Intensity dataset
(MOSI). The dataset is rigorously annotated with labels for subjectivity,
sentiment intensity, per-frame and per-opinion annotated visual features, and
per-milliseconds annotated audio features. Furthermore, we present baselines
for future studies in this direction as well as a new multimodal fusion
approach that jointly models spoken words and visual gestures.
Deep Hashing: A Joint Approach for Image Signature Learning
Authors: Yadong Mu, Zhu Liu
Date: 2016-08-12 00:00:00
Tasks: Deep Hashing, Feature Engineering, image-classification, Image Classification, Image Retrieval

Similarity-based image hashing represents crucial technique for visual data
storage reduction and expedited image search. Conventional hashing schemes
typically feed hand-crafted features into hash functions, which separates the
procedures of feature extraction and hash function learning. In this paper, we
propose a novel algorithm that concurrently performs feature engineering and
non-linear supervised hashing function learning. Our technical contributions in
this paper are two-folds: 1) deep network optimization is often achieved by
gradient propagation, which critically requires a smooth objective function.
The discrete nature of hash codes makes them not amenable for gradient-based
optimization. To address this issue, we propose an exponentiated hashing loss
function and its bilinear smooth approximation. Effective gradient calculation
and propagation are thereby enabled; 2) pre-training is an important trick in
supervised deep learning. The impact of pre-training on the hash code quality
has never been discussed in current deep hashing literature. We propose a
pre-training scheme inspired by recent advance in deep network based image
classification, and experimentally demonstrate its effectiveness. Comprehensive
quantitative evaluations are conducted on several widely-used image benchmarks.
On all benchmarks, our proposed deep hashing algorithm outperforms all
state-of-the-art competitors by significant margins. In particular, our
algorithm achieves a near-perfect 0.99 in terms of Hamming ranking accuracy
with only 12 bits on MNIST, and a new record of 0.74 on the CIFAR10 dataset. In
comparison, the best accuracies obtained on CIFAR10 by existing hashing
algorithms without or with deep networks are known to be 0.36 and 0.58
respectively.
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Authors: Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Date: 2015-10-31 00:00:00
Tasks: Variational Inference

Several recent works have explored stochastic gradient methods for
variational inference that exploit the geometry of the variational-parameter
space. However, the theoretical properties of these methods are not
well-understood and these methods typically only apply to
conditionally-conjugate models. We present a new stochastic method for
variational inference which exploits the geometry of the variational-parameter
space and also yields simple closed-form updates even for non-conjugate models.
We also give a convergence-rate analysis of our method and many other previous
methods which exploit the geometry of the space. Our analysis generalizes
existing convergence results for stochastic mirror-descent on non-convex
objectives by using a more general class of divergence functions. Beyond giving
a theoretical justification for a variety of recent methods, our experiments
show that new algorithms derived in this framework lead to state of the art
results on a variety of problems. Further, due to its generality, we expect
that our theoretical analysis could also apply to other applications.
Faster Training of Very Deep Networks Via p-Norm Gates
Authors: Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2016-08-11 00:00:00
Tasks: Machine Translation, Translation

A major contributing factor to the recent advances in deep neural networks is
structural units that let sensory information and gradients to propagate
easily. Gating is one such structure that acts as a flow control. Gates are
employed in many recent state-of-the-art recurrent models such as LSTM and GRU,
and feedforward models such as Residual Nets and Highway Networks. This enables
learning in very deep networks with hundred layers and helps achieve
record-breaking results in vision (e.g., ImageNet with Residual Nets) and NLP
(e.g., machine translation with GRU). However, there is limited work in
analysing the role of gating in the learning process. In this paper, we propose
a flexible $p$-norm gating scheme, which allows user-controllable flow and as a
consequence, improve the learning speed. This scheme subsumes other existing
gating schemes, including those in GRU, Highway Networks and Residual Nets as
special cases. Experiments on large sequence and vector datasets demonstrate
that the proposed gating scheme helps improve the learning speed significantly
without extra overhead.
Distributed-memory large deformation diffeomorphic 3D image registration
Authors: Andreas Mang, Amir Gholami, George Biros
Date: 2016-08-11 00:00:00
Tasks: Image Registration, Medical Image Analysis

We present a parallel distributed-memory algorithm for large deformation
diffeomorphic registration of volumetric images that produces large isochoric
deformations (locally volume preserving). Image registration is a key
technology in medical image analysis. Our algorithm uses a partial differential
equation constrained optimal control formulation. Finding the optimal
deformation map requires the solution of a highly nonlinear problem that
involves pseudo-differential operators, biharmonic operators, and pure
advection operators both forward and back- ward in time. A key issue is the
time to solution, which poses the demand for efficient optimization methods as
well as an effective utilization of high performance computing resources. To
address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov
solver. Our algorithm integrates several components: a spectral discretization
in space, a semi-Lagrangian formulation in time, analytic adjoints, different
regularization functionals (including volume-preserving ones), a spectral
preconditioner, a highly optimized distributed Fast Fourier Transform, and a
cubic interpolation scheme for the semi-Lagrangian time-stepping. We
demonstrate the scalability of our algorithm on images with resolution of up to
$1024^3$ on the "Maverick" and "Stampede" systems at the Texas Advanced
Computing Center (TACC). The critical problem in the medical imaging
application domain is strong scaling, that is, solving registration problems of
a moderate size of $256^3$---a typical resolution for medical images. We are
able to solve the registration problem for images of this size in less than
five seconds on 64 x86 nodes of TACC's "Maverick" system.
A New Framework for Distributed Submodular Maximization
Authors: Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen, Justin Ward
Date: 2015-07-14 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Document Summarization

A wide variety of problems in machine learning, including exemplar
clustering, document summarization, and sensor placement, can be cast as
constrained submodular maximization problems. A lot of recent effort has been
devoted to developing distributed algorithms for these problems. However, these
results suffer from high number of rounds, suboptimal approximation ratios, or
both. We develop a framework for bringing existing algorithms in the sequential
setting to the distributed setting, achieving near optimal approximation ratios
for many settings in only a constant number of MapReduce rounds. Our techniques
also give a fast sequential algorithm for non-monotone maximization subject to
a matroid constraint.
Automatic detection of moving objects in video surveillance
Authors: Larbi Guezouli, Hanane Belhani
Date: 2016-08-11 00:00:00
Tasks: Motion Detection

This work is in the field of video surveillance including motion detection.
The video surveillance is one of essential techniques for automatic video
analysis to extract crucial information or relevant scenes in video
surveillance systems. The aim of our work is to propose solutions for the
automatic detection of moving objects in real time with a surveillance camera.
The detected objects are objects that have some geometric shape (circle,
ellipse, square, and rectangle).
Clockwork Convnets for Video Semantic Segmentation
Authors: Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell
Date: 2016-08-11 00:00:00
Tasks: Image Segmentation, Scheduling, Segmentation, Semantic Segmentation, Video Recognition, Video Semantic Segmentation

Recent years have seen tremendous progress in still-image segmentation;
however the na\"ive application of these state-of-the-art algorithms to every
video frame requires considerable computation and ignores the temporal
continuity inherent in video. We propose a video recognition framework that
relies on two key observations: 1) while pixels may change rapidly from frame
to frame, the semantic content of a scene evolves more slowly, and 2) execution
can be viewed as an aspect of architecture, yielding purpose-fit computation
schedules for networks. We define a novel family of "clockwork" convnets driven
by fixed or adaptive clock signals that schedule the processing of different
layers at different update rates according to their semantic stability. We
design a pipeline schedule to reduce latency for real-time recognition and a
fixed-rate schedule to reduce overall computation. Finally, we extend clockwork
scheduling to adaptive video processing by incorporating data-driven clocks
that can be tuned on unlabeled video. The accuracy and efficiency of clockwork
convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video
datasets.
Warm Starting Bayesian Optimization
Authors: Matthias Poloczek, Jialei Wang, Peter I. Frazier
Date: 2016-08-11 00:00:00
Tasks: Bayesian Optimization

We develop a framework for warm-starting Bayesian optimization, that reduces
the solution time required to solve an optimization problem that is one in a
sequence of related problems. This is useful when optimizing the output of a
stochastic simulator that fails to provide derivative information, for which
Bayesian optimization methods are well-suited. Solving sequences of related
optimization problems arises when making several business decisions using one
optimization model and input data collected over different time periods or
markets. While many gradient-based methods can be warm started by initiating
optimization at the solution to the previous problem, this warm start approach
does not apply to Bayesian optimization methods, which carry a full metamodel
of the objective function from iteration to iteration. Our approach builds a
joint statistical model of the entire collection of related objective
functions, and uses a value of information calculation to recommend points to
evaluate.
Deep Structured-Output Regression Learning for Computational Color Constancy
Authors: Yanlin Qian, Ke Chen, Joni-Kristian Kamarainen, Jarno Nikkanen, Jiri Matas
Date: 2016-07-13 00:00:00
Tasks: Color Constancy, regression

Computational color constancy that requires esti- mation of illuminant colors
of images is a fundamental yet active problem in computer vision, which can be
formulated into a regression problem. To learn a robust regressor for color
constancy, obtaining meaningful imagery features and capturing latent
correlations across output variables play a vital role. In this work, we
introduce a novel deep structured-output regression learning framework to
achieve both goals simultaneously. By borrowing the power of deep convolutional
neural networks (CNN) originally designed for visual recognition, the proposed
framework can automatically discover strong features for white balancing over
different illumination conditions and learn a multi-output regressor beyond
underlying relationships between features and targets to find the complex
interdependence of dif- ferent dimensions of target variables. Experiments on
two public benchmarks demonstrate that our method achieves competitive
performance in comparison with the state-of-the-art approaches.
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms
Authors: Nihir Patel, Jason T. L. Wang
Date: 2016-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Inductive Learning, Transductive Learning

Use of computational methods to predict gene regulatory networks (GRNs) from
gene expression data is a challenging task. Many studies have been conducted
using unsupervised methods to fulfill the task; however, such methods usually
yield low prediction accuracies due to the lack of training data. In this
article, we propose semi-supervised methods for GRN prediction by utilizing two
machine learning algorithms, namely support vector machines (SVM) and random
forests (RF). The semi-supervised methods make use of unlabeled data for
training. We investigate inductive and transductive learning approaches, both
of which adopt an iterative procedure to obtain reliable negative training data
from the unlabeled data. We then apply our semi-supervised methods to gene
expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate
the performance of our methods using the expression data. Our analysis
indicated that the transductive learning approach outperformed the inductive
learning approach for both organisms. However, there was no conclusive
difference identified in the performance of SVM and RF. Experimental results
also showed that the proposed semi-supervised methods performed better than
existing supervised methods for both organisms.
Dual Control for Approximate Bayesian Reinforcement Learning
Authors: Edgar D. Klenske, Philipp Hennig
Date: 2015-10-13 00:00:00
Tasks: regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Control of non-episodic, finite-horizon dynamical systems with uncertain
dynamics poses a tough and elementary case of the exploration-exploitation
trade-off. Bayesian reinforcement learning, reasoning about the effect of
actions and future observations, offers a principled solution, but is
intractable. We review, then extend an old approximate approach from control
theory---where the problem is known as dual control---in the context of modern
regression methods, specifically generalized linear regression. Experiments on
simulated systems show that this framework offers a useful approximation to the
intractable aspects of Bayesian RL, producing structured exploration strategies
that differ from standard RL approaches. We provide simple examples for the use
of this framework in (approximate) Gaussian process regression and feedforward
neural networks for the control of exploration.