pwc-archive/papers-with-abstracts huggingface dataset:
Phylogenetic analyses of the severe acute respiratory syndrome coronavirus 2 reflected the several routes of introduction to Taiwan, the United States, and Japan
Authors: Tomoko Matsuda, Hikoyu Suzuki, Norichika Ogata
Date: 2020-02-20 00:00:00

Worldwide Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is disrupting in the economy and anxiety of people. The public anxiety has increased the psychological burden on government and healthcare professionals, resulting in a government worker suicide in Japan. The terrified people are asking the government for border measures. However, are border measures possible for this virus? By analyzing 48 almost complete virus genome sequences, we found out that the viruses that invaded Taiwan, the United States, and Japan were introduced independently. We identified thirteen parsimony-informative sites and three groups (CTC, TCC, and TCT). Viruses found outside China did not form a monophyletic clade, opposite to previous study. These results suggest the difficulty of implementing effective border measures against this virus.
Is there sufficient evidence for criticality in cortical systems?
Date: 2020-02-20 00:00:00
Tasks: Diversity

Many studies have found evidence that the brain operates at a critical point, a processus known as self-organized criticality. A recent paper found remarkable scalings suggestive of criticality in systems as different as neural cultures, anesthetized or awake brains. We point out here that the diversity of these states would question any claimed role of criticality in information processing. Furthermore, we show that two non-critical systems pass all the tests for criticality, a control that was not provided in the original article. We conclude that such false positives demonstrate that the presence of criticality in the brain is still not proven and that we need better methods that scaling analyses.
Forecasting Realized Volatility Matrix With Copula-Based Models
Date: 2020-02-20 00:00:00
Tasks: Time Series, Time Series Analysis

Multivariate volatility modeling and forecasting are crucial in financial
economics. This paper develops a copula-based approach to model and forecast
realized volatility matrices. The proposed copula-based time series models can
capture the hidden dependence structure of realized volatility matrices. Also,
this approach can automatically guarantee the positive definiteness of the
forecasts through either Cholesky decomposition or matrix logarithm
transformation. In this paper we consider both multivariate and bivariate
copulas; the types of copulas include Student's t, Clayton and Gumbel copulas.
In an empirical application, we find that for one-day ahead volatility matrix
forecasting, these copula-based models can achieve significant performance both
in terms of statistical precision as well as creating economically
mean-variance efficient portfolio. Among the copulas we considered, the
multivariate-t copula performs better in statistical precision, while
bivariate-t copula has better economical performance.
Autonomous Angles-Only Multi-Target Tracking for Spacecraft Swarms
Authors: Justin Kruger, Simone D'Amico
Date: 2020-02-18 00:00:00

This paper presents a new algorithm for autonomous multitarget tracking of resident space objects using optical angles-only measurements from a spaceborne observer. To enable autonomous angles-only navigation of spacecraft swarms, an observer must be able to identify and track multiple known or unknown target space objects in view, without reliance on a-priori relative orbit knowledge. Extremely high assignment precision is necessary with low measurement frequencies and limited computational resources. The new "Spacecraft Angles-only MUltitarget tracking System" (SAMUS) algorithm has been developed to meet these objectives and constraints. It combines domain-specific modeling of target kinematics with multi-hypothesis techniques to autonomously track multiple unknown targets using only sequential camera images. A measurement transform ensures that target motion in the observer reference frame follows consistent parametric models; curve fitting is used to predict track behavior; and kinematically-derived track gating and scoring criteria are applied to improve the efficiency and accuracy of the multi-hypothesis approach. Monte-Carlo testing with high-fidelity simulations demonstrates close to 100% measurement assignment precision and strong recall across a range of multi-spacecraft formations, in both near-circular and eccentric orbits. Tracking is maintained in the presence of eclipse periods, significant measurement noise, and partially known swarm maneuvers. A comparison to other tracking algorithms reveals strong advantages in precision, robustness and computation time, crucial for spaceborne angles-only navigation.
STW and SPIHT Wavelet compression using MATLAB wavelet Tool for Color Image
Date: 2020-02-19 00:00:00
Tasks: Image Compression

Images can be represented by mathematical function using wavelets. Wavelet
can be manipulated (shrink/expand) by applying some values to its function. It
helps to localize the signals. Application of wavelet in images processing has
larger scope as proved. Image compression is one of the dimension. There are
various wavelet image compression techniques. This research paper focused on
comparison of only two techniques i.e. STW and SPIHT for color JPEG images.
V-Formation via Model Predictive Control
Date: 2020-02-20 00:00:00
Tasks: model, Model Predictive Control

We present recent results that demonstrate the power of viewing the problem
of V-formation in a flock of birds as one of Model Predictive Control (MPC).
The V-formation-MPC marriage can be understood in terms of the problem of
synthesizing an optimal plan for a continuous-space and continuous-time Markov
decision process (MDP), where the goal is to reach a target state that
minimizes a given cost function. First, we consider ARES, an approximation
algorithm for generating optimal plans (action sequences) that take an initial
state of an MDP to a state whose cost is below a specified (convergence)
threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both
the receding horizon and the particle swarm. Inspired by Importance Splitting,
the length of the horizon and the number of particles are chosen such that at
least one particle reaches a next-level state. ARES can alternatively be viewed
as a model-predictive control (MPC) algorithm that utilizes an adaptive
receding horizon, aka Adaptive MPC (AMPC). We next present Distributed AMPC
(DAMPC), a distributed version of AMPC that works with local neighborhoods. We
introduce adaptive neighborhood resizing, whereby the neighborhood size is
determined by the cost-based Lyapunov function evaluated over a global system
state. Our experiments show that DAMPC can perform almost as well as
centralized AMPC, while using only local information and a form of distributed
consensus in each time step. Finally, inspired by security attacks on
cyber-physical systems, we introduce controller-attacker games (CAG), where two
players, a controller and an attacker, have antagonistic objectives. We
formulate a special case of CAG called V-formation games, where the attacker's
goal is to prevent the controller from attaining V-formation. We demonstrate
how adaptation in the design of the controller helps in overcoming certain
attacks.
Recent progress in molecular simulation methods for drug binding kinetics
Date: 2020-05-24 00:00:00
Tasks: Drug Discovery

Due to the contribution of drug-target binding kinetics to drug efficacy,
there is a high level of interest in developing methods to predict drug-target
binding kinetic parameters. During the review period, a wide range of enhanced
sampling molecular dynamics simulation-based methods has been developed for
computing drug-target binding kinetics and studying binding and unbinding
mechanisms. Here, we assess the performance of these methods considering two
benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large
set of N-HSP90-inhibitor complexes. The results indicate that some of the
simulation methods can already be usefully applied in drug discovery or lead
optimization programs but that further studies on more high-quality
experimental benchmark datasets are necessary to improve and validate
computational methods.
E2E Migration Strategies Towards 5G: Long-term Migration Plan and Evolution Roadmap
Date: 2020-02-20 00:00:00
Tasks: Marketing

After freezing the first phase of the fifth generation of wireless networks
(5G) standardization, it finally goes live now and the rollout of the
commercial launch (most in fixed 5G broadband services) and migration has been
started. However, some challenges are arising in the deployment, integration of
each technology, and the interoperability in the network of the communication
service providers (CSPs). At the same time, the evolution of 5G is not clear
and many questions arise such as whether 5G has long-term evolution or when 5G
will change to a next-generation one. This paper provides long-term migration
options and paths towards 5G considering many key factors such as the cost,
local/national data traffic, marketing, and the standardization trends in the
radio access network (RAN), the transport network (TN), the core network (CN),
and E2E network. Moreover, we outline some 5G evolution road maps emphasizing
on the technologies, standards, and service time lines. The proposed migration
paths can be the answer to some CSPs concerns about how to do long-term
migration to 5G and beyond.
Towards Critical Clearing Time Sensitivity for DAE Systems with Singularity
Date: 2020-02-20 00:00:00
Tasks: Sensitivity

Standard power system models are parameter dependent differential-algebraic
equation (DAE) type. Following a transient event, voltage collapse can occur as
a bifurcation of the transient load flow solutions which is marked by the
system trajectory reaching a singular surface in state space where the voltage
causality is lost. If a fault is expected to cause voltage collapse, preventive
control decisions such as changes in AVR settings need to be taken in order to
get enhance the system stability. In this regard, the knowledge of sensitivity
of critical clearing time (CCT) to controllable system parameters can be of
great help. The quasi-stability boundary of DAE systems is more complicated
than ODE systems where in addition to unstable equilibrium points (UEP) and
periodic orbits, singularity plays an important role making the problem
challenging. The stability boundary is then made up of a number of dynamically
distinct components. In the present work, we derive the expression for CCT
sensitivity for the phenomenon where the critical fault-on trajectory
intersects the singular surface itself which is one such component forming the
stability boundary. The results are illustrated for a small test system in
order to gain visual insights.
Electrodynamics Correlates Knock-on and Knock-off: Current is Spatially Uniform in Ion Channels
Date: 2020-05-14 00:00:00

Ions in channels have been imagined as hard balls in a macroscopic mechanical
model, for a very long time. Hard balls interact by collisions in such models,
randomly knocking each other on and off `binding' sites in thermal motion. But
ions have large charge, and the hard balls of classical models do not. The
electrodynamics of charge guarantee strong correlations between the movements
of ions on all time scales, even those of atomic scale thermal motion.
Correlations are present whenever Maxwell's equations apply, so they are
present in individual trajectories, not just averages. Indeed, in a series
system like an idealized narrow channel, the correlation is perfect (within the
accuracy of Maxwell's equations) because of conservation of total current (that
includes Maxwell's displacement current, the ethereal ${\varepsilon
}_0{\partial \boldsymbol{\mathrm{E}}}/{\partial t}$).The ethereal component of
current prevents spatial variation of total current in a series system. The
stochastic complexity of spatial thermal motion disappears for current }in a
series system. Total current does not depend on location in a series system
like a narrow ion channel on any time scale. Spatial variables are not needed
in the description of total current in a one dimensional channel on any time
scale, according to the Maxwell equations. Removing the spatial dependence of
total current should dramatically simplify theories and simulations, one might
imagine.
Sector connectedness in the Chinese stock markets
Date: 2020-02-21 00:00:00

Uncovering the risk transmitting path within economic sectors in China is
crucial for understanding the stability of the Chinese economic system,
especially under the current situation of the China-US trade conflicts. In this
paper, we try to uncover the risk spreading channels by means of volatility
spillovers within the Chinese sectors using stock market data. By applying the
generalized variance decomposition framework based on the VAR model and the
rolling window approach, a set of connectedness matrices is obtained to reveal
the overall and dynamic spillovers within sectors. It is found that 17 sectors
(mechanical equipment, electrical equipment, utilities, and so on) are risk
transmitters and 11 sectors (national defence, bank, non-bank finance, and so
on) are risk takers during the whole period. During the periods with the
extreme risk events (the global financial crisis, the Chinese interbank
liquidity crisis, the Chinese stock market plunge, and the China-US trade war),
we observe that the connectedness measures significantly increase and the
financial sectors play a buffer role in stabilizing the economic system. The
robust tests suggest that our results are not sensitive to the changes of model
parameters. Our results not only uncover the spillover effects within the
Chinese sectors, but also highlight the deep understanding of the risk
contagion patterns in the Chinese stock markets.
Asymptotic Linearity of Consumption Functions and Computational Efficiency
Authors: Qingyin Ma, Alexis Akira Toda
Date: 2020-02-21 00:00:00
Tasks: Computational Efficiency

We prove that the consumption functions in optimal savings problems are asymptotically linear if the marginal utility is regularly varying. We also analytically characterize the asymptotic marginal propensities to consume (MPCs) out of wealth. Our results are useful for obtaining good initial guesses when numerically computing consumption functions, and provide a theoretical justification for linearly extrapolating consumption functions outside the grid.
Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks
Date: 2020-08-21 00:00:00
Tasks: Image Quality Assessment

Quality assessment of omnidirectional images has become increasingly urgent
due to the rapid growth of virtual reality applications. Different from
traditional 2D images and videos, omnidirectional contents can provide
consumers with freely changeable viewports and a larger field of view covering
the $360^{\circ}\times180^{\circ}$ spherical surface, which makes the objective
quality assessment of omnidirectional images more challenging. In this paper,
motivated by the characteristics of the human vision system (HVS) and the
viewing process of omnidirectional contents, we propose a novel Viewport
oriented Graph Convolution Network (VGCN) for blind omnidirectional image
quality assessment (IQA). Generally, observers tend to give the subjective
rating of a 360-degree image after passing and aggregating different viewports
information when browsing the spherical scenery. Therefore, in order to model
the mutual dependency of viewports in the omnidirectional image, we build a
spatial viewport graph. Specifically, the graph nodes are first defined with
selected viewports with higher probabilities to be seen, which is inspired by
the HVS that human beings are more sensitive to structural information. Then,
these nodes are connected by spatial relations to capture interactions among
them. Finally, reasoning on the proposed graph is performed via graph
convolutional networks. Moreover, we simultaneously obtain global quality using
the entire omnidirectional image without viewport sampling to boost the
performance according to the viewing experience. Experimental results
demonstrate that our proposed model outperforms state-of-the-art full-reference
and no-reference IQA metrics on two public omnidirectional IQA databases.
Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs
Authors: Martin G. Frasch, Bernd Walter, Christophe L. Herry, Reinhard Bauer
Date: 2020-02-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Heart Rate Variability

Ischemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sedation and hypoxic/ischemic conditions with ensuing recovery. We provide technical validation using EEGLAB. We also delineate the corresponding changes in the electrocardiogram (ECG)-derived heart rate variability (HRV) with the potential for future in-depth analyses of joint EEG-ECG dynamics. We review an open-source methodology to derive signatures of coupling between the ECoG and electrothalamogram (EThG) signals contained in the presented data set to better characterize the dynamics of thalamocortical communication during these clinically relevant states. The data set is presented in full band sampled at 2000 Hz, so the additional potential exists for insights from the full-band EEG and high-frequency oscillations under the bespoke experimental conditions. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.
Depression Detection using Resting State Three-channel EEG Signal
Date: 2020-02-27 00:00:00
Tasks: Depression Detection, EEG, Electroencephalogram (EEG)

In universal environment, a patient-friendly inexpensive method is needed to
realize the early diagnosis of depression, which is believed to be an effective
way to reduce the mortality of depression. The purpose of this study is only to
collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and
nonlinear features of EEG used to classify depression patients and healthy
controls. The EEG recordings were carried out on a group of 18 medication-free
depressive patients and 25 gender and age matched controls. In this paper, the
selected features include three linear (maximum, mean and center values of the
power) and three nonlinear features (correlation dimension, Renyi entropy and
C0 complexity). The accuracy and effectiveness of classification model between
depressive and control subjects were calculated using leave-one-out
cross-validation. The experimental results indicate that selected three channel
EEG and features can distinguish the subjects between depression and normal
beings, the classification accuracy is 72.25%. It is hoped that the performed
results can provide more choices for the early diagnosis of depression in a
universal environment.
Simplified Ray Tracing for the Millimeter Wave Channel: A Performance Evaluation
Date: 2020-02-21 00:00:00

Millimeter-wave (mmWave) communication is one of the cornerstone innovations
of fifth-generation (5G) wireless networks, thanks to the massive bandwidth
available in these frequency bands. To correctly assess the performance of such
systems, however, it is essential to have reliable channel models, based on a
deep understanding of the propagation characteristics of the mmWave signal. In
this respect, ray tracers can provide high accuracy, at the expense of a
significant computational complexity, which limits the scalability of
simulations. To address this issue, in this paper we present possible
simplifications that can reduce the complexity of ray tracing in the mmWave
environment, without significantly affecting the accuracy of the model. We
evaluate the effect of such simplifications on link-level metrics, testing
different configuration parameters and propagation scenarios.
Analysis of performance criteria for optimization based bearing only target tracking algorithms
Date: 2020-02-21 00:00:00
Tasks: Evolutionary Algorithms, global-optimization

Target tracking problem has many practical applications in real life. In
submarines, target tracking is done using, preferably, passive sensors. These
sensors measure only the bearing angles between the observed target and the
ownship. Therefore, this problem is generally referred as bearing only target
tracking or target motion analysis. The classical approach is to use a state
observer based filter, i.e. Extended Kalman Filter, to estimate the range,
course and speed of the target, using only the bearings. In recent studies, the
problem is solved as a global optimization problem by utilizing evolutionary
algorithms with respect to some objective functions. In this study, we
investigate the effect of the commonly used cost functions on the performance
of the TMA algorithms. Particularly, we investigate the cost functions based on
bearing differences and equidistant line segments. The simulation results show
that the latter gives a sub-optimal solution to the target motion analysis
problem, compared to the former.
A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries
Date: 2020-02-21 00:00:00
Tasks: Demand Forecasting, Ensemble Learning

The Asian-pacific region is the major international tourism demand market in
the world, and its tourism demand is deeply affected by various factors.
Previous studies have shown that different market factors influence the tourism
market demand at different timescales. Accordingly, the decomposition ensemble
learning approach is proposed to analyze the impact of different market factors
on market demand, and the potential advantages of the proposed method on
forecasting tourism demand in the Asia-pacific region are further explored.
This study carefully explores the multi-scale relationship between tourist
destinations and the major source countries, by decomposing the corresponding
monthly tourist arrivals with noise-assisted multivariate empirical mode
decomposition. With the China and Malaysia as case studies, their respective
empirical results show that decomposition ensemble approach significantly
better than the benchmarks which include statistical model, machine learning
and deep learning model, in terms of the level forecasting accuracy and
directional forecasting accuracy.
Volatility has to be rough
Date: 2020-02-21 00:00:00

First, we give an asymptotic expansion of short-dated at-the-money implied
volatility that refines the preceding works and proves in particular that
non-rough volatility models are inconsistent to a power law of volatility skew.
Second, we show that given a power law of volatility skew in an option market,
a continuous price dynamics of the underlying asset with non-rough volatility
admits an arbitrage opportunity. The volatility therefore has to be rough in a
viable market of the underlying asset of which the volatility skew obeys a
power law.