pwc-archive/papers-with-abstracts huggingface dataset:
Compression of High Dynamic Range Video Using the HEVC and H.264/AVC Standards
Date: 2018-03-13 00:00:00
Tasks: Video Compression

The existing video coding standards such as H.264/AVC and High Efficiency
Video Coding (HEVC) have been designed based on the statistical properties of
Low Dynamic Range (LDR) videos and are not accustomed to the characteristics of
High Dynamic Range (HDR) content. In this study, we investigate the performance
of the latest LDR video compression standard, HEVC, as well as the recent
widely commercially used video compression standard, H.264/AVC, on HDR content.
Subjective evaluations of results on an HDR display show that viewers clearly
prefer the videos coded via an HEVC-based encoder to the ones encoded using an
H.264/AVC encoder. In particular, HEVC outperforms H.264/AVC by an average of
10.18% in terms of mean opinion score and 25.08% in terms of bit rate savings.
The Effect of Frame Rate on 3D Video Quality and Bitrate
Date: 2018-03-13 00:00:00

Increasing the frame rate of a 3D video generally results in improved Quality
of Experience (QoE). However, higher frame rates involve a higher degree of
complexity in capturing, transmission, storage, and display. The question that
arises here is what frame rate guarantees high viewing quality of experience
given the existing/required 3D devices and technologies (3D cameras, 3D TVs,
compression, transmission bandwidth, and storage capacity). This question has
already been addressed for the case of 2D video, but not for 3D. The objective
of this paper is to study the relationship between 3D quality and bitrate at
different frame rates. Our performance evaluations show that increasing the
frame rate of 3D videos beyond 60 fps may not be visually distinguishable. In
addition, our experiments show that when the available bandwidth is reduced,
the highest possible 3D quality of experience can be achieved by adjusting
(decreasing) the frame rate instead of increasing the compression ratio. The
results of our study are of particular interest to network providers for rate
adaptation in variable bitrate channels.
An Efficient Human Visual System Based Quality Metric for 3D Video
Date: 2018-03-13 00:00:00

Stereoscopic video technologies have been introduced to the consumer market
in the past few years. A key factor in designing a 3D system is to understand
how different visual cues and distortions affect the perceptual quality of
stereoscopic video. The ultimate way to assess 3D video quality is through
subjective tests. However, subjective evaluation is time consuming, expensive,
and in some cases not possible. The other solution is developing objective
quality metrics, which attempt to model the Human Visual System (HVS) in order
to assess perceptual quality. Although several 2D quality metrics have been
proposed for still images and videos, in the case of 3D efforts are only at the
initial stages. In this paper, we propose a new full-reference quality metric
for 3D content. Our method mimics HVS by fusing information of both the left
and right views to construct the cyclopean view, as well as taking to account
the sensitivity of HVS to contrast and the disparity of the views. In addition,
a temporal pooling strategy is utilized to address the effect of temporal
variations of the quality in the video. Performance evaluations showed that our
3D quality metric quantifies quality degradation caused by several
representative types of distortions very accurately, with Pearson correlation
coefficient of 90.8 %, a competitive performance compared to the
state-of-the-art 3D quality metrics.
Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video
Date: 2018-03-13 00:00:00
Tasks: Saliency Detection, Saliency Prediction, Video Saliency Prediction

Visual Attention Models (VAMs) predict the location of an image or video
regions that are most likely to attract human attention. Although saliency
detection is well explored for 2D image and video content, there are only few
attempts made to design 3D saliency prediction models. Newly proposed 3D visual
attention models have to be validated over large-scale video saliency
prediction datasets, which also contain results of eye-tracking information.
There are several publicly available eye-tracking datasets for 2D image and
video content. In the case of 3D, however, there is still a need for
large-scale video saliency datasets for the research community for validating
different 3D-VAMs. In this paper, we introduce a large-scale dataset containing
eye-tracking data collected from 61 stereoscopic 3D videos (and also 2D
versions of those) and 24 subjects participated in a free-viewing test. We
evaluate the performance of the existing saliency detection methods over the
proposed dataset. In addition, we created an online benchmark for validating
the performance of the existing 2D and 3D visual attention models and
facilitate addition of new VAMs to the benchmark. Our benchmark currently
contains 50 different VAMs.
Introducing A Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database
Date: 2018-03-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High Dynamic Range (HDR) displays and cameras are paving their ways through
the consumer market at a rapid growth rate. Thanks to TV and camera
manufacturers, HDR systems are now becoming available commercially to end
users. This is taking place only a few years after the blooming of 3D video
technologies. MPEG/ITU are also actively working towards the standardization of
these technologies. However, preliminary research efforts in these video
technologies are hammered by the lack of sufficient experimental data. In this
paper, we introduce a Stereoscopic 3D HDR (SHDR) database of videos that is
made publicly available to the research community. We explain the procedure
taken to capture, calibrate, and post-process the videos. In addition, we
provide insights on potential use-cases, challenges, and research
opportunities, implied by the combination of higher dynamic range of the HDR
aspect, and depth impression of the 3D aspect.
Resource Allocation in Wireless Networks with Energy Constraints
Date: 2018-03-10 00:00:00
Tasks: Fairness, Scheduling

This dissertation focuses on the development of novel scheduling and resource
allocation schemes, which take into account and regulate the energy constraints
imposed by the levels of harvested energy. To this direction, first, the
optimal energy, time, and bandwidth allocation problem for the downlink of
energy harvesting base stations (EHBSs) is investigated, with the main focus
being on autonomous EHBSs. The presented analysis considers the impact of the
energy constraint on users' preferences and the BS's revenue. In order to model
the competitive nature of the problem, game theory is used. The next two
chapters focus on wireless powered networks (WPNs) and simultaneous wireless
information and power transfer (SWIPT) using radio frequency (RF) technology.
One of the main contributions of these chapters is the introduction of both
uplink and downlink non-orthogonal multiple access (NOMA) for WPNs. Moreover,
the individual data rates and fairness are improved, while the formulated
problems are optimally and efficiently solved. It is shown that, compared to
orthogonal multiple access, NOMA offers a considerable improvement in
throughput, fairness, and energy efficiency. Rather than this, proportional
fairness is maximized and uplink/downlink of WPNs are jointly optimized, in
which cases, except for NOMA, time division multiple access (TDMA) is also
investigated. Furthermore, the role of interference is considered, which has
been recognized as one of the main reasons of the asymmetric overall
degradation of the users' performance, due to different path-loss values,
called from now on as cascaded near-far problem. Moreover, SWIPT is
investigated and efficiently optimized in the context of multicarrier
cooperative communication networks. Finally, simultaneous lightwave information
and power transfer (SLIPT) is introduced, while novel and fundamental
techniques are proposed.
Theoretical and empirical analysis of trading activity
Date: 2018-10-15 00:00:00

Understanding the structure of financial markets deals with suitably
determining the functional relation between financial variables. In this
respect, important variables are the trading activity, defined here as the
number of trades $N$, the traded volume $V$, the asset price $P$, the squared
volatility $\sigma^2$, the bid-ask spread $S$ and the cost of trading $C$.
Different reasonings result in simple proportionality relations ("scaling
laws") between these variables. A basic proportionality is established between
the trading activity and the squared volatility, i.e., $N \sim \sigma^2$. More
sophisticated relations are the so called 3/2-law $N^{3/2} \sim \sigma P V /C$
and the intriguing scaling $N \sim (\sigma P/S)^2$. We prove that these
"scaling laws" are the only possible relations for considered sets of variables
by means of a well-known argument from physics: dimensional analysis. Moreover,
we provide empirical evidence based on data from the NASDAQ stock exchange
showing that the sophisticated relations hold with a certain degree of
universality. Finally, we discuss the time scaling of the volatility $\sigma$,
which turns out to be more subtle than one might naively expect.
Design and Analysis of Passband Transmitted Reference Pulse Cluster UWB Systems in the Presence of Phase Noise
Date: 2018-03-13 00:00:00

Transmitted reference pulse cluster (TRPC) signaling was recently proposed
and developed for noncoherent ultra-wideband (UWB) communications. In this
paper, a practical passband TRPC-UWB system is designed and analyzed to deal
with the carrier frequency offset, phase offset and phase noise inherent in
voltage-controlled oscillators (VCO) of the transmitter and the receiver. Based
on a general model of noisy VCO and employing some reasonable assumptions, an
equivalent linear time-invariant (LTI) analytical model is obtained to
facilitate the bit error rate (BER) analysis. Our analysis shows that the
constant carrier frequency offset and the phase offset can be removed by
employing the passband transmitter and the noncoherent receiver. Furthermore, a
semi-analytical BER expression is derived to show the impact of phase noise on
the system error performance. Simulation results validate the semi-analytical
expressions and both of them indicate that TRPC is more robust to the effect of
phase noise than conventional transmitted reference (TR) and coherent UWB Rake
receivers.
Inference on a Distribution from Noisy Draws
Authors: Koen Jochmans, Martin Weidner
Date: 2018-03-13 00:00:00
Tasks: Selection bias

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the much-improved sampling behavior of the corrected estimators. An empirical illustration on heterogeneity in deviations from the law of one price is equally provided.
Removing both Internal and Unrealistic Energy-Generating Cycles in Flux Balance Analysis
Date: 2018-03-13 00:00:00

Constraint-based stoichiometric models are ubiquitous in metabolic research,
with Flux Balance Analysis (FBA) being the most widely used method to describe
metabolic phenotypes of cells growing in steady-state. Of the many variants of
constrain-based modelling methods published throughout the years, only few have
focused on thermodynamic issues, in particular the elimination of non-physical
and non-physiological cyclic fluxes. In this work, we revisit two of these
methods, namely thermodynamic FBA and loopless FBA, and analyze the strengths
and weaknesses of each one. Finally, we suggest a compromise denoted
semi-thermodynamic FBA (st-FBA) which imposes stronger thermodynamic constrains
on the flux polytope compared to loopless FBA, without requiring a large set of
thermodynamic parameters as in the case of thermodynamic FBA. We show that
st-FBA is a useful and simple way to eliminate thermodynamically infeasible
cycles that generate ATP.
An Endogenous Mechanism of Business Cycles
Date: 2019-09-26 00:00:00

This paper suggests that business cycles may be a manifestation of coupled
real economy and stock market dynamics and describes a mechanism that can
generate economic fluctuations consistent with observed business cycles. To
this end, we seek to incorporate into the macroeconomic framework a dynamic
stock market model based on opinion interactions (Gusev et al., 2015). We
derive this model from microfoundations, provide its empirical verification,
demonstrate that it contains the efficient market as a particular regime and
establish a link through which macroeconomic models can be attached for the
study of real economy and stock market interaction. To examine key effects, we
link it with a simple macroeconomic model (Blanchard, 1981). The coupled system
generates nontrivial endogenous dynamics, which exhibit deterministic and
stochastic features, producing quasiperiodic fluctuations (business cycles). We
also inspect this system's behavior in the phase space. The real economy and
the stock market coevolve dynamically along the path governed by a
stochastically-forced dynamical system with two stable equilibria, one where
the economy expands and the other where it contracts, resulting in business
cycles identified as the coherence resonance phenomenon. Thus, the
incorporation of stock market dynamics into the macroeconomic framework, as
presented here, allows the derivation of realistic behaviors in a tractable
setting.
Using RuleBuilder to graphically define and visualize BioNetGen-language patterns and reaction rules
Date: 2018-03-13 00:00:00

RuleBuilder is a tool for drawing graphs that can be represented by the
BioNetGen language (BNGL), which is used to formulate mathematical, rule-based
models of biochemical systems. BNGL provides an intuitive plain-text, or
string, representation of such systems, which is based on a graphical
formalism. Reactions are defined in terms of graph-rewriting rules that specify
the necessary intrinsic properties of the reactants, a transformation, and a
rate law. Rules may also contain contextual constraints that restrict
application of the rule. In some cases, the specification of contextual
constraints can be verbose, making a rule difficult to read. RuleBuilder is
designed to ease the task of reading and writing individual reaction rules, as
well as individual BNGL patterns similar to those found in rules. The software
assists in the reading of existing models by converting BNGL strings of
interest into a graph-based representation composed of nodes and edges.
RuleBuilder also enables the user to construct de novo a visual representation
of BNGL strings using drawing tools available in its interface. As objects are
added to the drawing canvas, the corresponding BNGL string is generated on the
fly, and objects are similarly drawn on the fly as BNGL strings are entered
into the application. RuleBuilder thus facilitates construction and
interpretation of rule-based models.
FDG kinetics in cells and tissues: a biochemically-driven compartmental approach
Date: 2018-03-13 00:00:00

The radioactive glucose analogue 2-deoxy-2-[18F]fluoro-D-glucose (FDG) is
widely used to reconstruct glucose metabolism and other biological functions in
cells and tissues. The analysis of data on the time course of FDG tracer
distribution is performed by the use of appropriate compartmental models.
Motivated by recent results in cell biochemistry, we describe a new
compartmental model aiming at the reconstruction of tracer kinetics in cells
and tissues, which emphasizes the different roles of the cytosol and of the
endoplasmic reticulum. Two applications of the new model are examined, that are
concerned with real data from cancer cell cultures in vitro, and cancer tissues
in vivo. The results are compared with those obtained through application of
more standard compartmental models against the same datasets and appear to be
in a better agreement with respect to recent biochemical experimental evidence.
In particular, it is shown that tracer tends to accumulate in the endoplasmic
reticulum, rather than cytosol, and that the rate of phosphorylation is higher
than predicted by current models.
Blind Identification of SFBC-OFDM Signals Using Subspace Decompositions and Random Matrix Theory
Date: 2019-08-14 00:00:00
Tasks: Decision Making

Blind signal identification has important applications in both civilian and
military communications. Previous investigations on blind identification of
space-frequency block codes (SFBCs) only considered identifying Alamouti and
spatial multiplexing transmission schemes. In this paper, we propose a novel
algorithm to identify SFBCs by analyzing discriminating features for different
SFBCs, calculated by separating the signal subspace and noise subspace of the
received signals at different adjacent OFDM subcarriers. Relying on random
matrix theory, this algorithm utilizes a serial hypothesis test to determine
the decision boundary according to the maximum eigenvalue in the noise
subspace. Then, a decision tree of a special distance metric is employed for
decision making. The proposed algorithm does not require prior knowledge of the
signal parameters such as the number of transmit antennas, channel
coefficients, modulation mode and noise power. Simulation results verify the
viability of the proposed algorithm for a reduced observation period with an
acceptable computational complexity.
Stock Price Prediction using Principle Components
Date: 2018-03-13 00:00:00
Tasks: Dimensionality Reduction, Prediction, Stock Price Prediction

The literature provides strong evidence that stock prices can be predicted
from past price data. Principal component analysis (PCA) is a widely used
mathematical technique for dimensionality reduction and analysis of data by
identifying a small number of principal components to explain the variation
found in a data set. In this paper, we describe a general method for stock
price prediction using covariance information, in terms of a dimension
reduction operation based on principle component analysis. Projecting the noisy
observation onto a principle subspace leads to a well-conditioned problem. We
illustrate our method on daily stock price values for five companies in
different industries. We investigate the results based on mean squared error
and directional change statistic of prediction, as measures of performance, and
volatility of prediction as a measure of risk.
A Study on the Relationship Between Depth Map Quality and the Overall 3D Video Quality OF Experience
Date: 2018-03-14 00:00:00

The emergence of multiview displays has made the need for synthesizing
virtual views more pronounced, since it is not practical to capture all of the
possible views when filming multiview content. View synthesis is performed
using the available views and depth maps. There is a correlation between the
quality of the synthesized views and the quality of depth maps. In this paper
we study the effect of depth map quality on perceptual quality of synthesized
view through subjective and objective analysis. Our evaluation results show
that: 1) 3D video quality depends highly on the depth map quality and 2) the
Visual Information Fidelity index computed between the reference and distorted
depth maps has Pearson correlation ratio of 0.75 and Spearman rank order
correlation coefficient of 0.67 with the subjective 3D video quality.
Spectrum Sensing: Enhanced Energy Detection Technique Based on Noise Measurement
Date: 2018-03-14 00:00:00

Spectrum sensing enables cognitive radio systems to detect unused portions of
the radio spectrum and then use them while avoiding interferences to the
primary users. Energy detection is one of the most used techniques for spectrum
sensing because it does not require any prior information about the
characteristics of the primary user signal. However, this technique does not
distinguish between the signal and the noise. It has a low performance at low
SNR, and the selection of the threshold becomes an issue because the noise is
uncertain. The detection performance of this technique can be further improved
using a dynamic selection of the sensing threshold. In this work, we
investigate a dynamic selection of this threshold by measuring the power of
noise present in the received signal using a blind technique. The proposed
model was implemented and tested using GNU Radio software and USRP units. Our
results show that the dynamic selection of the threshold based on measuring the
noise level present in the received signal during the detection process
increases the probability of detection and decreases the probability of false
alarm compared to the ones of energy detection with a static threshold.
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets
Authors: Han Xinzhi, Lei Sen
Date: 2018-03-14 00:00:00
Tasks: feature selection, Information Retrieval, Learning-To-Rank, Retrieval

With the rapid advance of the Internet, search engines (e.g., Google, Bing,
Yahoo!) are used by billions of users for each day. The main function of a
search engine is to locate the most relevant webpages corresponding to what the
user requests. This report focuses on the core problem of information
retrieval: how to learn the relevance between a document (very often webpage)
and a query given by user. Our analysis consists of two parts: 1) we use
standard statistical methods to select important features among 137 candidates
given by information retrieval researchers from Microsoft. We find that not all
the features are useful, and give interpretations on the top-selected features;
2) we give baselines on prediction over the real-world dataset MSLR-WEB by
using various learning algorithms. We find that models of boosting trees,
random forest in general achieve the best performance of prediction. This
agrees with the mainstream opinion in information retrieval community that
tree-based algorithms outperform the other candidates for this problem.