pwc-archive/papers-with-abstracts huggingface dataset:
RFID-BASED Prepaid Power Meter
Date: 2018-06-10 00:00:00
Tasks: valid

An Electric power meter is an important component in electric energy service.
In the past, many consumers have complained about reading inaccurate of the
electric meter. This research presents the development of an electrical power
meter equipped with RFID reader. The RFID reader reads a valid RFID card and
activates the power meter so that it can supply electricity. When the credit is
about low or before the electricity is auto cut off, an SMS message will be
sent to the users handphone to alert.
Universal Computer aided design for electrical machines
Date: 2018-06-10 00:00:00

Electrical machines are devices that change either mechanical or electrical
energy to the other and also can alternate the voltage levels of an alternating
current. The need for electrical machines cannot be overemphasized since they
are used in various applications in the world today. Its design is to meet the
specifications as stated by the user and this design has to be an economical
one. The design, therefore, revolves around designing the machine to meet the
stipulated performance required, the cost available and the lasting life of the
machine. This work aims to eliminate the tediousness involved in the manual
hand calculations of designing the machines by making use of a graphical user
interface and using iterations in situations where the data would have been
assumed.
Small Satellite Optical Communication Networks: Analytical Models
Date: 2018-08-29 00:00:00

Small satellites, especially picosatellites, appear poised to play an
important role in the future of space systems. Due to their size, however,
integrating them with high-throughput laser-based communication systems remains
a challenge. In this paper, we develop several analytical models that quantify
how optical communication networks can be implemented with picosatellites. We
do so with the goal of identifying design challenges that need to be addressed
if picosatellites and optical communication systems are indeed to be a standard
part of the future space technological landscape.
On the Natural Structure of Amino Acid Patterns in Families of Protein Sequences
Date: 2018-07-26 00:00:00

All known terrestrial proteins are coded as continuous strings of ~20 amino
acids. The patterns formed by the repetitions of elements in groups of finite
sequences describes the natural architectures of protein families. We present a
method to search for patterns and groupings of patterns in protein sequences
using a mathematically precise definition for 'repetition', an efficient
algorithmic implementation and a robust scoring system with no adjustable
parameters. We show that the sequence patterns can be well-separated into
disjoint classes according to their recurrence in nested structures. The
statistics of pattern occurrences indicate that short repetitions are enough to
account for the differences between natural families and randomized groups by
more than 10 standard deviations, while patterns shorter than 5 residues are
effectively random. A small subset of patterns is sufficient to account for a
robust ''familiarity'' definition of arbitrary sets of sequences.
Parallel Transport on the Cone Manifold of SPD Matrices for Domain Adaptation
Date: 2018-07-27 00:00:00
Tasks: Domain Adaptation

In this paper, we consider the problem of domain adaptation. We propose to
view the data through the lens of covariance matrices and present a method for
domain adaptation using parallel transport on the cone manifold of symmetric
positive-definite matrices. We provide rigorous analysis using Riemanninan
geometry, illuminating the theoretical guarantees and benefits of the presented
method. In addition, we demonstrate these benefits using experimental results
on simulations and real-measured data.
Investigating Wheat Price with a Multi-Agent Model
Date: 2018-07-27 00:00:00
Tasks: model

In this paper, we build a computational model for the analysis of
international wheat spot price formation, its dynamics and the dynamics of
internationally exchanged quantities. The model has been calibrated using
FAOSTAT data to evaluate its in-sample predictive power. The model is able to
generate wheat prices in twelve international markets and wheat used quantities
in twenty-four world regions. The time span considered goes from 1992 to 2013.
In our study, a particular attention was paid to the impact of Russian
Federation's 2010 grain export ban on wheat price and internationally traded
quantities. Among other results, we find that wheat average weighted world
price in 2013 would have been 3.55\% lower than the observed one if the Russian
Federation would not have imposed the export ban in 2010.
DeepPhase: Surgical Phase Recognition in CATARACTS Videos
Authors: Zisimopoulos Odysseas, Flouty Evangello, Luengo Imanol, Giataganas Petros, Nehme Jean, Chow Andre, Stoyanov Danail
Date: 2018-07-17 00:00:00
Tasks: Surgical phase recognition, Surgical tool detection

Automated surgical workflow analysis and understanding can assist surgeons to
standardize procedures and enhance post-surgical assessment and indexing, as
well as, interventional monitoring. Computer-assisted interventional (CAI)
systems based on video can perform workflow estimation through surgical
instruments' recognition while linking them to an ontology of procedural
phases. In this work, we adopt a deep learning paradigm to detect surgical
instruments in cataract surgery videos which in turn feed a surgical phase
inference recurrent network that encodes temporal aspects of phase steps within
the phase classification. Our models present comparable to state-of-the-art
results for surgical tool detection and phase recognition with accuracies of 99
and 78% respectively.
Embedded Implementation of a Deep Learning Smile Detector
Authors: Ghazi Pedram, Happonen Antti P., Boutellier Jani, Huttunen Heikki
Date: 2018-07-10 00:00:00
Tasks: Deep Learning

In this paper we study the real time deployment of deep learning algorithms
in low resource computational environments. As the use case, we compare the
accuracy and speed of neural networks for smile detection using different
neural network architectures and their system level implementation on NVidia
Jetson embedded platform. We also propose an asynchronous multithreading scheme
for parallelizing the pipeline. Within this framework, we experimentally
compare thirteen widely used network topologies. The experiments show that low
complexity architectures can achieve almost equal performance as larger ones,
with a fraction of computation required.
Sparse Range-constrained Learning and Its Application for Medical Image Grading
Authors: Cheng Jun
Date: 2018-07-11 00:00:00
Tasks: Sparse Learning

Sparse learning has been shown to be effective in solving many real-world
problems. Finding sparse representations is a fundamentally important topic in
many fields of science including signal processing, computer vision, genome
study and medical imaging. One important issue in applying sparse
representation is to find the basis to represent the data,especially in
computer vision and medical imaging where the data is not necessary incoherent.
In medical imaging, clinicians often grade the severity or measure the risk
score of a disease based on images. This process is referred to as medical
image grading. Manual grading of the disease severity or risk score is often
used. However, it is tedious, subjective and expensive. Sparse learning has
been used for automatic grading of medical images for different diseases. In
the grading, we usually begin with one step to find a sparse representation of
the testing image using a set of reference images or atoms from the dictionary.
Then in the second step, the selected atoms are used as references to compute
the grades of the testing images. Since the two steps are conducted
sequentially, the objective function in the first step is not necessarily
optimized for the second step. In this paper, we propose a novel sparse
range-constrained learning(SRCL)algorithm for medical image grading.Different
from most of existing sparse learning algorithms, SRCL integrates the objective
of finding a sparse representation and that of grading the image into one
function. It aims to find a sparse representation of the testing image based on
atoms that are most similar in both the data or feature representation and the
medical grading scores. We apply the new proposed SRCL to CDR computation and
cataract grading. Experimental results show that the proposed method is able to
improve the accuracy in cup-to-disc ratio computation and cataract grading.
Two-Layer Mixture Network Ensemble for Apparel Attributes Classification
Authors: Han Tianqi, Fu Zhihui, Li Hongyu
Date: 2018-07-11 00:00:00
Tasks: Classification, Ensemble Learning, General Classification, image-classification, Image Classification, Vocal Bursts Valence Prediction

Recognizing apparel attributes has recently drawn great interest in the
computer vision community. Methods based on various deep neural networks have
been proposed for image classification, which could be applied to apparel
attributes recognition. An interesting problem raised is how to ensemble these
methods to further improve the accuracy. In this paper, we propose a two-layer
mixture framework for ensemble different networks. In the first layer of this
framework, two types of ensemble learning methods, bagging and boosting, are
separately applied. Different from traditional methods, our bagging process
makes use of the whole training set, not random subsets, to train each model in
the ensemble, where several differentiated deep networks are used to promote
model variance. To avoid the bias of small-scale samples, the second layer only
adopts bagging to mix the results obtained with bagging and boosting in the
first layer. Experimental results demonstrate that the proposed mixture
framework outperforms any individual network model or either independent
ensemble method in apparel attributes classification.
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Authors: Wei Pan, Cagle Lucas, Reza Tasmia, Ball John, Gafford James
Date: 2018-07-11 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

Collision avoidance is a critical task in many applications, such as ADAS
(advanced driver-assistance systems), industrial automation and robotics. In an
industrial automation setting, certain areas should be off limits to an
automated vehicle for protection of people and high-valued assets. These areas
can be quarantined by mapping (e.g., GPS) or via beacons that delineate a
no-entry area. We propose a delineation method where the industrial vehicle
utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to
detect passive beacons and model-predictive control to stop the vehicle from
entering a restricted space. The beacons are standard orange traffic cones with
a highly reflective vertical pole attached. The LiDAR can readily detect these
beacons, but suffers from false positives due to other reflective surfaces such
as worker safety vests. Herein, we put forth a method for reducing false
positive detection from the LiDAR by projecting the beacons in the camera
imagery via a deep learning method and validating the detection using a neural
network-learned projection from the camera to the LiDAR space. Experimental
data collected at Mississippi State University's Center for Advanced Vehicular
Systems (CAVS) shows the effectiveness of the proposed system in keeping the
true detection while mitigating false positives.
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
Authors: Ball John E., Wei Pan
Date: 2018-07-11 00:00:00
Tasks: Data Augmentation, Denoising, Hyperspectral Image Classification, Hyperspectral Image Segmentation, image-classification, Image Classification, Image Segmentation, Semantic Segmentation

Herein, we present a system for hyperspectral image segmentation that
utilizes multiple class--based denoising autoencoders which are efficiently
trained. Moreover, we present a novel hyperspectral data augmentation method
for labelled HSI data using linear mixtures of pixels from each class, which
helps the system with edge pixels which are almost always mixed pixels.
Finally, we utilize a deep neural network and morphological hole-filling to
provide robust image classification. Results run on the Salinas dataset verify
the high performance of the proposed algorithm.
Multi-Region Ensemble Convolutional Neural Network for Facial Expression Recognition
Authors: Fan Yingruo, Lam Jacqueline C. K., Li Victor O. K.
Date: 2018-07-12 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expressions play an important role in conveying the emotional states
of human beings. Recently, deep learning approaches have been applied to image
recognition field due to the discriminative power of Convolutional Neural
Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble
CNN (MRE-CNN) framework for facial expression recognition, which aims to
enhance the learning power of CNN models by capturing both the global and the
local features from multiple human face sub-regions. Second, the weighted
prediction scores from each sub-network are aggregated to produce the final
prediction of high accuracy. Third, we investigate the effects of different
sub-regions of the whole face on facial expression recognition. Our proposed
method is evaluated based on two well-known publicly available facial
expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the
state-of-the-art recognition accuracy.
Visual Attention driven by Convolutional Features
Authors: Zanca Dario, Gori Marco
Date: 2018-07-12 00:00:00
Tasks: Saliency Prediction

The understanding of where humans look in a scene is a problem of great
interest in visual perception and computer vision. When eye-tracking devices
are not a viable option, models of human attention can be used to predict
fixations. In this paper we give two contribution. First, we show a model of
visual attention that is simply based on deep convolutional neural networks
trained for object classification tasks. A method for visualizing saliency maps
is defined which is evaluated in a saliency prediction task. Second, we
integrate the information of these maps with a bottom-up differential model of
eye-movements to simulate visual attention scanpaths. Results on saliency
prediction and scores of similarity with human scanpaths demonstrate the
effectiveness of this model.
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Authors: Su Jiang, Fraser Nicholas J., Gambardella Giulio, Blott Michaela, Durelli Gianluca, Thomas David B., Leong Philip, Cheung Peter Y. K.
Date: 2018-07-17 00:00:00
Tasks: Quantization

Modern CNN are typically based on floating point linear algebra based
implementations. Recently, reduced precision NN have been gaining popularity as
they require significantly less memory and computational resources compared to
floating point. This is particularly important in power constrained compute
environments. However, in many cases a reduction in precision comes at a small
cost to the accuracy of the resultant network. In this work, we investigate the
accuracy-throughput trade-off for various parameter precision applied to
different types of NN models. We firstly propose a quantization training
strategy that allows reduced precision NN inference with a lower memory
footprint and competitive model accuracy. Then, we quantitatively formulate the
relationship between data representation and hardware efficiency. Our
experiments finally provide insightful observation. For example, one of our
tests show 32-bit floating point is more hardware efficient than 1-bit
parameters to achieve 99% MNIST accuracy. In general, 2-bit and 4-bit fixed
point parameters show better hardware trade-off on small-scale datasets like
MNIST and CIFAR-10 while 4-bit provide the best trade-off in large-scale tasks
like AlexNet on ImageNet dataset within our tested problem domain.
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Authors: Khanal Bishesh, Gomez Alberto, Toussaint Nicolas, McDonagh Steven, Zimmer Veronika, Skelton Emily, Matthew Jacqueline, Grzech Daniel, Wright Robert, Gupta Chandni, Hou Benjamin, Rueckert Daniel, Schnabel Julia A., Kainz Bernhard
Date: 2018-07-19 00:00:00

Ultrasound (US) is the most widely used fetal imaging technique. However, US
images have limited capture range, and suffer from view dependent artefacts
such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a
high-resolution volume can extend the field of view and remove image artefacts,
which is useful for retrospective analysis including population based studies.
However, such volume reconstructions require information about relative
transformations between probe positions from which the individual volumes were
acquired. In prenatal US scans, the fetus can move independently from the
mother, making external trackers such as electromagnetic or optical tracking
unable to track the motion between probe position and the moving fetus. We
provide a novel methodology for image-based tracking and volume reconstruction
by combining recent advances in deep learning and simultaneous localisation and
mapping (SLAM). Tracking semantics are established through the use of a
Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of
concept, experiments are conducted on US volumes taken from a whole body fetal
phantom, and from the heads of real fetuses. For the fetal head segmentation,
we also introduce a novel weak annotation approach to minimise the required
manual effort for ground truth annotation. We evaluate our method
qualitatively, and quantitatively with respect to tissue discrimination
accuracy and tracking robustness.
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
Authors: Wickstrøm Kristoffer, Kampffmeyer Michael, Jenssen Robert
Date: 2018-07-16 00:00:00
Tasks: Medical Image Analysis, object-detection, Object Detection, Segmentation, Semantic Segmentation

Convolutional Neural Networks (CNNs) are propelling advances in a range of
different computer vision tasks such as object detection and object
segmentation. Their success has motivated research in applications of such
models for medical image analysis. If CNN-based models are to be helpful in a
medical context, they need to be precise, interpretable, and uncertainty in
predictions must be well understood. In this paper, we develop and evaluate
recent advances in uncertainty estimation and model interpretability in the
context of semantic segmentation of polyps from colonoscopy images. We evaluate
and enhance several architectures of Fully Convolutional Networks (FCNs) for
semantic segmentation of colorectal polyps and provide a comparison between
these models. Our highest performing model achieves a 76.06\% mean IOU accuracy
on the EndoScene dataset, a considerable improvement over the previous
state-of-the-art.
Finding any Waldo: zero-shot invariant and efficient visual search
Authors: Zhang Mengmi, Feng Jiashi, Ma Keng Teck, Lim Joo Hwee, Zhao Qi, Kreiman Gabriel
Date: 2018-07-18 00:00:00

Searching for a target object in a cluttered scene constitutes a fundamental
challenge in daily vision. Visual search must be selective enough to
discriminate the target from distractors, invariant to changes in the
appearance of the target, efficient to avoid exhaustive exploration of the
image, and must generalize to locate novel target objects with zero-shot
training. Previous work has focused on searching for perfect matches of a
target after extensive category-specific training. Here we show for the first
time that humans can efficiently and invariantly search for natural objects in
complex scenes. To gain insight into the mechanisms that guide visual search,
we propose a biologically inspired computational model that can locate targets
without exhaustive sampling and generalize to novel objects. The model provides
an approximation to the mechanisms integrating bottom-up and top-down signals
during search in natural scenes.
A Minimal Power Model for Human Running Performance
Date: 2018-07-23 00:00:00

Models for human running performances of various complexities and underlying
principles have been proposed, often combining data from world record
performances and bio-energetic facts of human physiology. Here we present a
novel, minimal and universal model for human running performance that employs a
relative metabolic power scale. The main component is a self-consistency
relation for the time dependent maximal power output. The analytic approach
presented here is the first to derive the observed logarithmic scaling between
world (and other) record running speeds and times from basic principles of
metabolic power supply. Various female and male record performances (world,
national) and also personal best performances of individual runners for
distances from 800m to the marathon are excellently described by this model,
with mean errors of (often much) less than 1%. The model defines endurance in a
way that demonstrates symmetry between long and short racing events that are
separated by a characteristic time scale comparable to the time over which a
runner can sustain maximal oxygen uptake. As an application of our model, we
derive personalized characteristic race speeds for different durations and
distances.
Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks
Authors: Au Benjamin, Shaham Uri, Dhruva Sanket, Bouras Georgios, Cristea Ecaterina, MD Alexandra Lansky, Coppi Andreas, Warner Fred, Li Shu-Xia, Krumholz Harlan
Date: 2018-07-19 00:00:00
Tasks: BIG-bench Machine Learning

The determination of a coronary stenosis and its severity in current clinical
workflow is typically accomplished manually via physician visual assessment
(PVA) during invasive coronary angiography. While PVA has shown large
inter-rater variability, the more reliable and accurate alternative of
Quantitative Coronary Angiography (QCA) is challenging to perform in real-time
due to the busy workflow in cardiac catheterization laboratories. We propose a
deep learning approach based on Convolutional Neural Networks (CNN) that
automatically characterizes and analyzes coronary stenoses in real-time by
automating clinical tasks performed during QCA. Our deep learning methods for
localization, segmentation and classification of stenosis in still-frame
invasive coronary angiography (ICA) images of the right coronary artery (RCA)
achieve performance of 72.7% localization accuracy, 0.704 dice coefficient and
0.825 C-statistic in each respective task. Integrated in an end-to-end
approach, our model's performance shows statistically significant improvement
in false discovery rate over the current standard in real-time clinical
stenosis assessment, PVA. To the best of the authors' knowledge, this is the
first time an automated machine learning system has been developed that can
implement tasks performed in QCA, and the first time an automated machine
learning system has demonstrated significant improvement over the current
clinical standard for rapid RCA stenosis analysis.