pwc-archive/papers-with-abstracts huggingface dataset:
State-Varying Factor Models of Large Dimensions
Date: 2020-10-17 00:00:00

This paper develops an inferential theory for state-varying factor models of
large dimensions. Unlike constant factor models, loadings are general functions
of some recurrent state process. We develop an estimator for the latent factors
and state-varying loadings under a large cross-section and time dimension. Our
estimator combines nonparametric methods with principal component analysis. We
derive the rate of convergence and limiting normal distribution for the
factors, loadings and common components. In addition, we develop a statistical
test for a change in the factor structure in different states. We apply the
estimator to U.S. Treasury yields and S&P500 stock returns. The systematic
factor structure in treasury yields differs in times of booms and recessions as
well as in periods of high market volatility. State-varying factors based on
the VIX capture significantly more variation and pricing information in
individual stocks than constant factor models.
On the Equilibrium of Query Reformulation and Document Retrieval
Authors: Zou Shihao, Tao Guanyu, Wang Jun, Zhang Weinan, Zhang Dell
Date: 2018-07-20 00:00:00
Tasks: Information Retrieval, Retrieval, Text Retrieval

In this paper, we study jointly query reformulation and document relevance
estimation, the two essential aspects of information retrieval (IR). Their
interactions are modelled as a two-player strategic game: one player, a query
formulator, taking actions to produce the optimal query, is expected to
maximize its own utility with respect to the relevance estimation of documents
produced by the other player, a retrieval modeler; simultaneously, the
retrieval modeler, taking actions to produce the document relevance scores,
needs to optimize its likelihood from the training data with respect to the
refined query produced by the query formulator. Their equilibrium or equilibria
will be reached when both are the best responses to each other. We derive our
equilibrium theory of IR using normal-form representations: when a standard
relevance feedback algorithm is coupled with a retrieval model, they would
share the same objective function and thus form a partnership game; by
contrast, pseudo relevance feedback pursues a rather different objective than
that of retrieval models, therefore the interaction between them would lead to
a general-sum game (though implicitly collaborative). Our game-theoretical
analyses not only yield useful insights into the two major aspects of IR, but
also offer new practical algorithms for achieving the equilibrium state of
retrieval which have been shown to bring consistent performance improvements in
both text retrieval and item recommendation.
Applications of Data Mining Techniques for Vehicular Ad hoc Networks
Authors: Zamil Mohammed AL, Samarah Samer
Date: 2018-06-19 00:00:00
Tasks: Classification, Clustering, General Classification, Outlier Detection

Due to the recent advances in vehicular ad hoc networks (VANETs), smart
applications have been incorporating the data generated from these networks to
provide quality of life services. In this paper, we have proposed taxonomy of
data mining techniques that have been applied in this domain in addition to a
classification of these techniques. Our contribution is to highlight the
research methodologies in the literature and allow for comparing among them
using different characteristics. The proposed taxonomy covers elementary data
mining techniques such as: preprocessing, outlier detection, clustering, and
classification of data. In addition, it covers centralized, distributed,
offline, and online techniques from the literature.
Ribosome self-assembly leads to overlapping reproduction cycles and increases growth rate
Authors: Rami Pugatch, Yinon M. Bar-On
Date: 2018-07-08 00:00:00

In permissive environments, E. coli can double its dry mass every 21 minutes. During this time, ribosomes, RNA polymerases, and the proteome are all doubled. Yet, the question of how to relate bacterial doubling time to other biologically relevant time scales in the growth process remains illusive, due to the complex temporal nesting pattern of these processes. In particular, the relation between the cell's doubling time and the ribosome assembly time is not known. Here we develop a model that connects growth rate to ribosome assembly time and show that the existence of a self-assembly step increases the overall growth rate, because during ribosome self-assembly existing ribosomes can start a new round of reproduction, by making a new batch of ribosomal proteins prior to the completion of the previous round. This overlapping of ribosome reproduction cycles increases growth rate beyond the serial-limit that is typically assumed to hold. Using recent data from ribosome profiling and well known measurements of the average translation rate, rigid bounds on the in-vivo ribosome self-assembly time are set, which are robust to the assumptions regarding the biological noises involved. At 21 minutes doubling time, the ribosome assembly time is found to be approximately 6 minutes --- three fold larger than the common estimate. We further use our model to explain the detrimental effect of a recently discovered ribosome assembly inhibitor drug, and predict the effect of limiting the expression of ribosome assembly chaperons on the overall growth rate.
Measurement Errors as Bad Leverage Points
Date: 2020-03-16 00:00:00
Tasks: regression

Errors-in-variables is a long-standing, difficult issue in linear regression;
and progress depends in part on new identifying assumptions. I characterize
measurement error as bad-leverage points and assume that fewer than half the
sample observations are heavily contaminated, in which case a high-breakdown
robust estimator may be able to isolate and down weight or discard the
problematic data. In simulations of simple and multiple regression where eiv
affects 25% of the data and R-squared is mediocre, certain high-breakdown
estimators have small bias and reliable confidence intervals.
Cancer Risk Messages: A Light Bulb Model
Date: 2018-07-10 00:00:00
Tasks: model

The meaning of public messages such as "One in x people gets cancer" or "One
in y people gets cancer by age z" can be improved. One assumption commonly
invoked is that there is no other cause of death, a confusing assumption. We
develop a light bulb model to clarify cumulative risk and we use Markov chain
modeling, incorporating the assumption widely in place, to evaluate transition
probabilities. Age-progression in the cancer risk is then reported on
Australian data. Future modelling can elicit realistic assumptions.
Cancer Risk Messages: Public Health and Economic Welfare
Date: 2018-07-10 00:00:00

Statements for public health purposes such as "1 in 2 will get cancer by age
85" have appeared in public spaces. The meaning drawn from such statements
affects economic welfare, not just public health. Both markets and government
use risk information on all kinds of risks, useful information can, in turn,
improve economic welfare, however inaccuracy can lower it. We adapt the
contingency table approach so that a quoted risk is cross-classified with the
states of nature. We show that bureaucratic objective functions regarding the
accuracy of a reported cancer risk can then be stated.
Fine-scale population structure analysis in Armadillidium vulgare (Isopoda: Oniscidea) reveals strong female philopatry
Date: 2019-10-18 00:00:00

In the last decades, dispersal studies have benefited from the use of
molecular markers for detecting patterns differing between categories of
individuals and have highlighted sex-biased dispersal in several species. To
explain this phenomenon, several hypotheses implying mating systems,
intrasexual competition or sex-related handicaps have been proposed. In this
context, we investigated sex-biased dispersal in Armadillidium vulgare, a
terrestrial isopod with a promiscuous mating system. As a proxy for effective
dispersal, we performed a fine-scale investigation of the spatial genetic
structure in males and females, using individuals originating from five
sampling points located within 70 meters of each other. Based on microsatellite
markers and spatial autocorrelation analyses, our results revealed that while
males did not present a significant genetic structure at this geographic scale,
females were significantly and genetically more similar to each other when they
were collected in the same sampling point. As females invest more parental care
than males in A. vulgare, but also because this species is promiscuous and
males experience a high intrasexual competition, our results meet the
predictions of most classical hypotheses for sex-biased dispersal. We suggest
that widening dispersal studies to other isopods or crustaceans, differing in
their ecology or mating system and displaying varying levels of parental care,
might shed light on the processes underlying the evolution of sex-biased
dispersal.
Mammography Assessment using Multi-Scale Deep Classifiers
Authors: An Ulzee, Shameer Khader, Subramanian Lakshmi
Date: 2018-06-30 00:00:00

Applying deep learning methods to mammography assessment has remained a
challenging topic. Dense noise with sparse expressions, mega-pixel raw data
resolution, lack of diverse examples have all been factors affecting
performance. The lack of pixel-level ground truths have especially limited
segmentation methods in pushing beyond approximately bounding regions. We
propose a classification approach grounded in high performance tissue
assessment as an alternative to all-in-one localization and assessment models
that is also capable of pinpointing the causal pixels. First, the objective of
the mammography assessment task is formalized in the context of local tissue
classifiers. Then, the accuracy of a convolutional neural net is evaluated on
classifying patches of tissue with suspicious findings at varying scales, where
highest obtained AUC is above $0.9$. The local evaluations of one such expert
tissue classifier is used to augment the results of a heatmap regression model
and additionally recover the exact causal regions at high resolution as a
saliency image suitable for clinical settings.
Deep Global-Connected Net With The Generalized Multi-Piecewise ReLU Activation in Deep Learning
Authors: Chen Zhi, Ho Pin-han
Date: 2018-06-19 00:00:00
Tasks: General Classification, Object Recognition

Recent Progress has shown that exploitation of hidden layer neurons in
convolution neural networks incorporating with a carefully designed activation
function can yield better classification results in the field of computer
vision. The paper firstly introduces a novel deep learning architecture aiming
to mitigate the gradient-vanishing problem, in which the earlier hidden layer
neurons could be directly connected with the last hidden layer and feed into
the last layer for classification. We then design a generalized linear
rectifier function as the activation function that can approximate arbitrary
complex functions via training of the parameters. We will show that our design
can achieve similar performance in a number of object recognition and video
action benchmark tasks, under significantly less number of parameters and
shallower network infrastructure, which is not only promising in training in
terms of computation burden and memory usage, but is also applicable to
low-computation, low-memory mobile scenarios.
Real-time Local Noise Filter in 3D Visualization of CT Data
Authors: Jerome N. Tan, Ateyev Z., Schmelzle S., Chilingaryan S., Kopmann A.
Date: 2018-06-24 00:00:00

Removing noise in computer tomography (CT) data for real-time 3D
visualization is vital to improving the quality of the final display. However,
the CT noise cannot be removed by straight averaging because the noise has a
broadband spatial frequency that is overlapping with the interesting signal
frequencies. To improve the display of structures and features contained in the
data, we present spatially variant filtering that performs averaging of
sub-regions around a central region. We compare our filter with four other
similar spatially variant filters regarding entropy and processing time. The
results demonstrate significant improvement of the visual quality with
processing time still within the millisecond range.
Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening
Authors: Haloi Mrinal, Rajalakshmi K. Raja, Walia Pradeep
Date: 2018-06-25 00:00:00
Tasks: Deep Learning

In this work, we propose advanced pneumonia and Tuberculosis grading system
for X-ray images. The proposed system is a very deep fully convolutional
classification network with online augmentation that outputs confidence values
for diseases prevalence. Its a fully automated system capable of disease
feature understanding without any offline preprocessing step or manual feature
extraction. We have achieved state- of-the- art performance on the public
databases such as ChestXray-14, Mendeley, Shenzhen Hospital X-ray and Belarus
X-ray set.
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Authors: Blott Michaela, Preusser Thomas B., Fraser Nicholas, Gambardella Giulio, OBrien Kenneth, Umuroglu Yaman, Leeser Miriam
Date: 2018-06-26 00:00:00
Tasks: Binarization

Convolutional Neural Networks have dramatically improved in recent years,
surpassing human accuracy on certain problems and performance exceeding that of
traditional computer vision algorithms. While the compute pattern in itself is
relatively simple, significant compute and memory challenges remain as CNNs may
contain millions of floating-point parameters and require billions of
floating-point operations to process a single image. These computational
requirements, combined with storage footprints that exceed typical cache sizes,
pose a significant performance and power challenge for modern compute
architectures. One of the promising opportunities to scale performance and
power efficiency is leveraging reduced precision representations for all
activations and weights as this allows to scale compute capabilities, reduce
weight and feature map buffering requirements as well as energy consumption.
While a small reduction in accuracy is encountered, these Quantized Neural
Networks have been shown to achieve state-of-the-art accuracy on standard
benchmark datasets, such as MNIST, CIFAR-10, SVHN and even ImageNet, and thus
provide highly attractive design trade-offs. Current research has focused
mainly on the implementation of extreme variants with full binarization of
weights and or activations, as well typically smaller input images. Within this
paper, we investigate the scalability of dataflow architectures with respect to
supporting various precisions for both weights and activations, larger image
dimensions, and increasing numbers of feature map channels. Key contributions
are a formalized approach to understanding the scalability of the existing
hardware architecture with cost models and a performance prediction as a
function of the target device size. We provide validating experimental results
for an ImageNet classification on a server-class platform, namely the AWS F1
node.
An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest
Authors: Ahn Ho Seok, Dayoub Feras, Popovic Marija, MacDonald Bruce, Siegwart Roland, Sa Inkyu
Date: 2018-06-26 00:00:00

Horticultural enterprises are becoming more sophisticated as the range of the
crops they target expands. Requirements for enhanced efficiency and
productivity have driven the demand for automating on-field operations.
However, various problems remain yet to be solved for their reliable, safe
deployment in real-world scenarios. This paper examines major research trends
and current challenges in horticultural robotics. Specifically, our work
focuses on sensing and perception in the three main horticultural procedures:
pollination, yield estimation, and harvesting. For each task, we expose major
issues arising from the unstructured, cluttered, and rugged nature of field
environments, including variable lighting conditions and difficulties in
fruit-specific detection, and highlight promising contemporary studies.
Watch to Edit: Video Retargeting using Gaze
Authors: Kumar Kranthi, Kumar Moneish, Gandhi Vineet, Subramanian Ramanathan
Date: 2018-06-27 00:00:00

We present a novel approach to optimally retarget videos for varied displays
with differing aspect ratios by preserving salient scene content discovered via
eye tracking. Our algorithm performs editing with cut, pan and zoom operations
by optimizing the path of a cropping window within the original video while
seeking to (i) preserve salient regions, and (ii) adhere to the principles of
cinematography. Our approach is (a) content agnostic as the same methodology is
employed to re-edit a wide-angle video recording or a close-up movie sequence
captured with a static or moving camera, and (b) independent of video length
and can in principle re-edit an entire movie in one shot. Our algorithm
consists of two steps. The first step employs gaze transition cues to detect
time stamps where new cuts are to be introduced in the original video via
dynamic programming. A subsequent step optimizes the cropping window path (to
create pan and zoom effects), while accounting for the original and new cuts.
The cropping window path is designed to include maximum gaze information, and
is composed of piecewise constant, linear and parabolic segments. It is
obtained via L(1) regularized convex optimization which ensures a smooth
viewing experience. We test our approach on a wide variety of videos and
demonstrate significant improvement over the state-of-the-art, both in terms of
computational complexity and qualitative aspects. A study performed with 16
users confirms that our approach results in a superior viewing experience as
compared to gaze driven re-editing and letterboxing methods, especially for
wide-angle static camera recordings.
Estimating Bicycle Route Attractivity from Image Data
Authors: Růžička Vít
Date: 2018-06-27 00:00:00
Tasks: Object Recognition, Transfer Learning

This master thesis focuses on practical application of Convolutional Neural
Network models on the task of road labeling with bike attractivity score. We
start with an abstraction of real world locations into nodes and scored edges
in partially annotated dataset. We enhance information available about each
edge with photographic data from Google Street View service and with additional
neighborhood information from Open Street Map database. We teach a model on
this enhanced dataset and experiment with ImageNet Large Scale Visual
Recognition Competition. We try different dataset enhancing techniques as well
as various model architectures to improve road scoring. We also make use of
transfer learning to use features from a task with rich dataset of ImageNet
into our task with smaller number of images, to prevent model overfitting.
Unsupervised Natural Image Patch Learning
Authors: Danon Dov, Averbuch-Elor Hadar, Fried Ohad, Cohen-Or Daniel
Date: 2018-06-28 00:00:00
Tasks: Domain Adaptation, Metric Learning

Learning a metric of natural image patches is an important tool for analyzing
images. An efficient means is to train a deep network to map an image patch to
a vector space, in which the Euclidean distance reflects patch similarity.
Previous attempts learned such an embedding in a supervised manner, requiring
the availability of many annotated images. In this paper, we present an
unsupervised embedding of natural image patches, avoiding the need for
annotated images. The key idea is that the similarity of two patches can be
learned from the prevalence of their spatial proximity in natural images.
Clearly, relying on this simple principle, many spatially nearby pairs are
outliers, however, as we show, the outliers do not harm the convergence of the
metric learning. We show that our unsupervised embedding approach is more
effective than a supervised one or one that uses deep patch representations.
Moreover, we show that it naturally leads itself to an efficient
self-supervised domain adaptation technique onto a target domain that contains
a common foreground object.
Performance of Turbo Coded OFDM Under the Presence of Various Noise Types
Date: 2018-06-28 00:00:00

A telecommunication system uses carriers in order to transmit information
through a cable or wirelessly. If each time only one carrier is transmitted,
then the system s signal will not be immune to frequency selective fading. If
frequency selective fading includes the working frequency of the system, then
the wireless link will not be established. Orthogonal frequency division
multiplexing OFDM is the primary solution for coping with inter signal
interference and frequency selective fading. Many carriers can be produced by
splitting a fast information stream to slower data series. Different orthogonal
frequencies carry slower data series. System s performance can be further
enhanced with the utilization of turbo codes. Turbo codes make the system more
immune to noise effects with excellent BER results. This paper presents the
thorough analysis of a turbo coded OFDM scheme using a PCCC technique in the
presence of a channel which includes AWGN, phase noise, Rayleigh fading, Rician
fading and Doppler shift.
Deep Networks with Shape Priors for Nucleus Detection
Authors: Tofighi Mohammad, Guo Tiantong, Vanamala Jairam K. P., Monga Vishal
Date: 2018-06-29 00:00:00

Detection of cell nuclei in microscopic images is a challenging research
topic, because of limitations in cellular image quality and diversity of
nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between
multiple cell nuclei. This has been a topic of enduring interest with promising
recent success shown by deep learning methods. These methods train for example
convolutional neural networks (CNNs) with a training set of input images and
known, labeled nuclei locations. Many of these methods are supplemented by
spatial or morphological processing. We develop a new approach that we call
Shape Priors with Convolutional Neural Networks (SP-CNN) to perform
significantly enhanced nuclei detection. A set of canonical shapes is prepared
with the help of a domain expert. Subsequently, we present a new network
structure that can incorporate `expected behavior' of nucleus shapes via two
components: {\em learnable} layers that perform the nucleus detection and a
{\em fixed} processing part that guides the learning with prior information.
Analytically, we formulate a new regularization term that is targeted at
penalizing false positives while simultaneously encouraging detection inside
cell nucleus boundary. Experimental results on a challenging dataset reveal
that SP-CNN is competitive with or outperforms several state-of-the-art
methods.
Quantity beats quality for semantic segmentation of corrosion in images
Authors: Nash Will, Drummond Tom, Birbilis Nick
Date: 2018-06-30 00:00:00
Tasks: Open-Ended Question Answering, Segmentation, Semantic Segmentation

Dataset creation is typically one of the first steps when applying Artificial
Intelligence methods to a new task; and the real world performance of models
hinges on the quality and quantity of data available. Producing an image
dataset for semantic segmentation is resource intensive, particularly for
specialist subjects where class segmentation is not able to be effectively
farmed out. The benefit of producing a large, but poorly labelled, dataset
versus a small, expertly segmented dataset for semantic segmentation is an open
question. Here we show that a large, noisy dataset outperforms a small,
expertly segmented dataset for training a Fully Convolutional Network model for
semantic segmentation of corrosion in images. A large dataset of 250 images
with segmentations labelled by undergraduates and a second dataset of just 10
images, with segmentations labelled by subject matter experts were produced.
The mean Intersection over Union and micro F-score metrics were compared after
training for 50,000 epochs. This work is illustrative for researchers setting
out to develop deep learning models for detection and location of specialist
features.