pwc-archive/papers-with-abstracts huggingface dataset:
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
Date: 2020-04-29 00:00:00
Tasks: Management, Segmentation

Management of vestibular schwannoma (VS) is based on tumour size as observed
on T1 MRI scans with contrast agent injection. Current clinical practice is to
measure the diameter of the tumour in its largest dimension. It has been shown
that volumetric measurement is more accurate and more reliable as a measure of
VS size. The reference approach to achieve such volumetry is to manually
segment the tumour, which is a time intensive task. We suggest that
semi-automated segmentation may be a clinically applicable solution to this
problem and that it could replace linear measurements as the clinical standard.
Using high-quality software available for academic purposes, we ran a
comparative study of manual versus semi-automated segmentation of VS on MRI
with 5 clinicians and scientists. We gathered both quantitative and qualitative
data to compare the two approaches; including segmentation time, segmentation
effort and segmentation accuracy. We found that the selected semi-automated
segmentation approach is significantly faster (167s versus 479s, p<0.001), less
temporally and physically demanding and has approximately equal performance
when compared with manual segmentation, with some improvements in accuracy.
There were some limitations, including algorithmic unpredictability and error,
which produced more frustration and increased mental effort in comparison to
manual segmentation. We suggest that semi-automated segmentation could be
applied clinically for volumetric measurement of VS on MRI. In future, the
generic software could be refined for use specifically for VS segmentation,
thereby improving accuracy.
Mixed volume of small reaction networks
Authors: Nida Obatake, Anne Shiu, Dilruba Sofia
Date: 2020-04-29 00:00:00

An important invariant of a chemical reaction network is its maximum number of positive steady states. This number, however, is in general difficult to compute. Nonetheless, there is an upper bound on this number -- namely, a network's mixed volume -- that is easy to compute. Moreover, recent work has shown that, for certain biological signaling networks, the mixed volume does not greatly exceed the maximum number of positive steady states. Continuing this line of research, we further investigate this overcount and also compute the mixed volumes of small networks, those with only a few species or reactions.
Virus Dynamics with Behavioral Responses
Authors: Krishna Dasaratha
Date: 2020-04-30 00:00:00

Motivated by epidemics such as COVID-19, we study the spread of a contagious disease when behavior responds to the disease's prevalence. We extend the SIR epidemiological model to include endogenous meeting rates. Individuals benefit from economic activity, but activity involves interactions with potentially infected individuals. The main focus is a theoretical analysis of contagion dynamics and behavioral responses to changes in risk. We obtain a simple condition for when public-health interventions or variants of a disease will have paradoxical effects on infection rates due to risk compensation. Behavioral responses are most likely to undermine public-health interventions near the peak of severe diseases.
$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning
Date: 2020-04-30 00:00:00
Tasks: GPR, regression

We present $\mathcal{L}_1$-$\mathcal{GP}$, an architecture based on
$\mathcal{L}_1$ adaptive control and Gaussian Process Regression (GPR) for safe
simultaneous control and learning. On one hand, the $\mathcal{L}_1$ adaptive
control provides stability and transient performance guarantees, which allows
for GPR to efficiently and safely learn the uncertain dynamics. On the other
hand, the learned dynamics can be conveniently incorporated into the
$\mathcal{L}_1$ control architecture without sacrificing robustness and
tracking performance. Subsequently, the learned dynamics can lead to less
conservative designs for performance/robustness tradeoff. We illustrate the
efficacy of the proposed architecture via numerical simulations.
CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech
Date: 2020-04-30 00:00:00
Tasks: Rhythm, text-to-speech, Text to Speech

Prosody Transfer (PT) is a technique that aims to use the prosody from a
source audio as a reference while synthesising speech. Fine-grained PT aims at
capturing prosodic aspects like rhythm, emphasis, melody, duration, and
loudness, from a source audio at a very granular level and transferring them
when synthesising speech in a different target speaker's voice. Current
approaches for fine-grained PT suffer from source speaker leakage, where the
synthesised speech has the voice identity of the source speaker as opposed to
the target speaker. In order to mitigate this issue, they compromise on the
quality of PT. In this paper, we propose CopyCat, a novel, many-to-many PT
system that is robust to source speaker leakage, without using parallel data.
We achieve this through a novel reference encoder architecture capable of
capturing temporal prosodic representations which are robust to source speaker
leakage. We compare CopyCat against a state-of-the-art fine-grained PT model
through various subjective evaluations, where we show a relative improvement of
$47\%$ in the quality of prosody transfer and $14\%$ in preserving the target
speaker identity, while still maintaining the same naturalness.
The convergence rate from discrete to continuous optimal investment stopping problem
Date: 2020-04-30 00:00:00

We study the optimal investment stopping problem in both continuous and
discrete case, where the investor needs to choose the optimal trading strategy
and optimal stopping time concurrently to maximize the expected utility of
terminal wealth. Based on the work [9] with an additional stochastic payoff
function, we characterize the value function for the continuous problem via the
theory of quadratic reflected backward stochastic differential equation (BSDE
for short) with unbounded terminal condition. In regard to discrete problem, we
get the discretization form composed of piecewise quadratic BSDEs recursively
under Markovian framework and the assumption of bounded obstacle, and provide
some useful prior estimates about the solutions with the help of auxiliary
forward-backward SDE system and Malliavin calculus. Finally, we obtain the
uniform convergence and relevant rate from discretely to continuously quadratic
reflected BSDE, which arise from corresponding optimal investment stopping
problem through above characterization.
A 6G White Paper on Connectivity for Remote Areas
Date: 2020-04-30 00:00:00

In many places all over the world rural and remote areas lack proper
connectivity that has led to increasing digital divide. These areas might have
low population density, low incomes, etc., making them less attractive places
to invest and operate connectivity networks. 6G could be the first mobile radio
generation truly aiming to close the digital divide. However, in order to do
so, special requirements and challenges have to be considered since the
beginning of the design process. The aim of this white paper is to discuss
requirements and challenges and point out related, identified research topics
that have to be solved in 6G. This white paper first provides a generic
discussion, shows some facts and discusses targets set in international bodies
related to rural and remote connectivity and digital divide. Then the paper
digs into technical details, i.e., into a solutions space. Each technical
section ends with a discussion and then highlights identified 6G challenges and
research ideas as a list.
Containment effort reduction and regrowth patterns of the Covid-19 spreading
Date: 2020-04-30 00:00:00

In all Countries the political decisions aim at the Covid-19 spreading
reduction and at reaching an almost stable configuration of coexistence with
the disease, where a small number of new infected individuals per day is
sustainable. In such a phase, the containment effort will be reduced in favor
of a gradual reopening of the social life and of the various economical sectors
and the evaluation of the possible infection regrowth should be very useful.
Microscopic models, which take into account the details of the new social and
economical conditions, would require a complex dynamics of the stakeholders. We
discuss a less detailed approach which, on the basis of the collected data
after few days from the beginning of the new phase, outlines different
scenarios of the Covid-19 diffusion for longer time. The method applies a time
dependent carrying capacity, analogously to many macroscopic growth laws in
biology, economics and population dynamics. The illustrative cases of
Singapore, Italy and Sicily are analyzed.
Refined compartmental models, asymptomatic carriers and COVID-19
Date: 2020-04-30 00:00:00

The goal of this article is to analyze some compartmental models specially
designed to model the spread of a disease whose transmission has the same
features as COVID-19. The major contributions of this article are: (1)
Rigorously find sufficient conditions for the outbreak to only have one peak,
i.e. for no second wave of infection to form; (2) Investigate the formation of
other waves of infection when such conditions are not met; (3) Use numerical
simulations to analyze the different roles asymptomatic carriers can have. We
also argue that dividing the population into non-interacting groups leads to an
effective reduction of the transmission rates. As in any compartmental model,
the goal of this article is to provide qualitative understanding rather than
exact quantitative predictions.
Dynamical Theory and Cellular Automata Simulations of Pandemic Spread: Understanding Different Temporal Patterns of Infections
Date: 2020-04-30 00:00:00

Here we propose and implement a generalized mathematical model to find the
time evolution of population in infectious diseases and apply the model to
study the recent COVID-19 pandemic. Our model at the core is a non-local
generalization of the widely used Kermack-McKendrick(KM) model where the
susceptible(S) population evolves into two other categories, namely
infectives(I) and removed(R). This is the well-known SIR model in which we
further divide both S and I into high and low risk categories. We first
formulate a set of non-local dynamical equations for the time evolution of
distinct population distributions under this categorization in an attempt to
describe the general scenario of infectious disease progression. We then solve
the non-linear coupled differential equations-(i) numerically by the method of
propagation, and (ii) a more flexible and versatile cellular automata (CA)
simulation which provides a coarse-grained description of the generalized
non-local model. In order to account for multiple factors such as role of
spreaders before containment, we introduce a time dependent rate which appears
to be essential to explain the sudden spikes before the plateau observed in
many cases (for example like China). We demonstrate how this generalized
approach allows us to handle the effects of (i) time-dependence of the
rate-constants of spread, (ii) different population density, (iii) the age
ratio, (iv) quarantine, (v) lockdown, and (vi) social distancing. Our study
allows us to make certain predictions regarding the nature of spread with
respect to several external parameters, treated as control variables. Analysis
of the model clearly shows that due to the strong heterogeneity in the epidemic
process originating from the distribution of initial infectives, the theory
must be local in character but at the same time connect to a global
perspective.
Robust Time-Frequency Reconstruction by Learning Structured Sparsity
Authors: Lei Jiang, Haijian Zhang, Lei Yu
Date: 2020-04-30 00:00:00
Tasks: Descriptive

Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals involved in realistic scenarios. It is well known that low time-frequency (TF) resolution and the emergency of cross-terms (CTs) are two main issues, which make it difficult to analyze and interpret practical signals using TFDs. In order to address these issues, we propose the U-Net aided iterative shrinkage-thresholding algorithm (U-ISTA) for reconstructing a near-ideal TFD by exploiting structured sparsity in signal TF domain. Specifically, the signal ambiguity function is firstly compressed, followed by unfolding the ISTA as a recurrent neural network. To consider continuously distributed characteristics of signals, a structured sparsity constraint is incorporated into the unfolded ISTA by regarding the U-Net as an adaptive threshold block, in which structure-aware thresholds are learned from enormous training data to exploit the underlying dependencies among neighboring TF coefficients. The proposed U-ISTA model is trained by both non-overlapped and overlapped synthetic signals including closely and far located non-stationary components. Experimental results demonstrate that the robust U-ISTA achieves superior performance compared with state-of-the-art algorithms, and gains a high TF resolution with CTs greatly eliminated even in low signal-to-noise ratio (SNR) environments.
Robust Phonetic Segmentation Using Spectral Transition measure for Non-Standard Recording Environments
Date: 2020-04-29 00:00:00
Tasks: Segmentation

Phone level localization of mis-articulation is a key requirement for an
automatic articulation error assessment system. A robust phone segmentation
technique is essential to aid in real-time assessment of phone level
mis-articulations of speech, wherein the audio is recorded on mobile phones or
tablets. This is a non-standard recording set-up with little control over the
quality of recording. We propose a novel post processing technique to aid
Spectral Transition Measure(STM)-based phone segmentation under noisy
conditions such as environment noise and clipping, commonly present during a
mobile phone recording. A comparison of the performance of our approach and
phone segmentation using traditional MFCC and PLPCC speech features for
Gaussian noise and clipping is shown. The proposed approach was validated on
TIMIT and Hindi speech corpus and was used to compute phone boundaries for a
set of speech, recorded simultaneously on three devices - a laptop, a
stationarily placed tablet and a handheld mobile phone, to simulate different
audio qualities in a real-time non-standard recording environment. F-ratio was
the metric used to compute the accuracy in phone boundary marking. Experimental
results show an improvement of 7% for TIMIT and 10% for Hindi data over the
baseline approach. Similar results were seen for the set of three of recordings
collected in-house.
Evolutionary Kuramoto Dynamics
Date: 2020-04-30 00:00:00

Common models of synchronizable oscillatory systems consist of a collection
of coupled oscillators governed by a collection of differential equations. The
ubiquitous Kuramoto models rely on an {\em a priori} fixed connectivity pattern
facilitates mutual communication and influence between oscillators. In
biological synchronizable systems, like the mammalian suprachaismatic nucleus,
enabling communication comes at a cost -- the organism expends energy creating
and maintaining the system -- linking their development to evolutionary
selection. Here, we introduce and analyze a new evolutionary game theoretic
framework modeling the behavior and evolution of systems of coupled
oscillators. Each oscillator in our model is characterized by a pair of dynamic
behavioral traits: an oscillatory phase and whether they connect and
communicate to other oscillators or not. Evolution of the system occurs along
these dimensions, allowing oscillators to change their phases and/or their
communication strategies. We measure success of mutations by comparing the
benefit of phase synchronization to the organism balanced against the cost of
creating and maintaining connections between the oscillators. Despite such a
simple setup, this system exhibits a wealth of nontrivial behaviors, mimicking
different classical games -- the Prisoner's Dilemma, the snowdrift game, and
coordination games -- as the landscape of the oscillators changes over time.
Despite such complexity, we find a surprisingly simple characterization of
synchronization through connectivity and communication: if the benefit of
synchronization $B(0)$ is greater than twice the cost $c$, $B(0) > 2c$, the
organism will evolve towards complete communication and phase synchronization.
Taken together, our model demonstrates possible evolutionary constraints on
both the existence of a synchronized oscillatory system and its overall
connectivity.
Deterministic Critical Community Size For The SIR System and Viral Strain Selection
Date: 2020-04-29 00:00:00

In this paper the concept of Critical Community Size (CCS) for the
deterministic SIR model is introduced and its consequences for the disease
dynamics are stressed. The disease can fade out after an outburst. Also the
principle of competitive exclusion holds no longer true. This is exemplified
for the dynamics of two competing virus strains. The virus with higher R0 can
be eradicated from the population.
Resilience in Optical Wireless Systems
Authors: Sarah O. M. Saeed, Sanaa Hamid Mohamed, Osama Zwaid Alsulami, Mohammed T. Alresheedi, Taisir E. H. ElGorashi, Jaafar M. H. Elmirghani
Date: 2020-04-30 00:00:00
Tasks: Maximum Separation

High reliability and availability of communication services is a key requirement that needs to be ensured by service providers. Since the direct line-of-sight (LOS) beam is prone to blockage in indoor optical wireless communication systems, a backup link needs to be at hand in case of blockage, and hence channel allocation algorithms should be blockage-aware. In this paper, the impact of beam blockage due to a disc with varying size and distance from the receiver is studied where blockage is quantitatively evaluated using percentage blockage for 512 room locations at 25 cm separation. It was found that assigning two links with maximum separation between the serving access points can reduce or eliminate blockage compared to the case when resilience is not implemented. Increasing the number of allocated access points per user further increases resilience.
A computational insight of the improved nicotine binding with ACE2-SARS-CoV-2 complex with its clinical impact
Date: 2020-04-30 00:00:00

Smokers being witnessed with the mild adverse clinical symptoms of
SARS-CoV-2, the in-silico study is intended to explore the effect of nicotine
binding to the soluble angiotensin converting enzyme II (ACE2) receptor with or
without SARS-CoV-2 binding. Nicotine established a stable interaction with the
conserved amino acid residues: Asp382, Gly405, His378 and Tyr385 through His401
of the soluble ACE2 that seals its interaction with the INS1. Also, nicotine
binding has significantly reduced the affinity score of ACE2 with INS1 to -12.6
kcal/mol (versus -15.7 kcal/mol without nicotine) and the interface area to
1933.6 square Angstrom (versus 2057.3 square Angstrom without nicotine).
Nicotine exhibited a higher binding affinity score with ACE2-SARS-CoV-2 complex
with -6.33 kcal/mol (Vs -5.24 kcal/mol without SARS-CoV-2) and a lowered
inhibitory contant value of 22.95 micromolar (Vs 151.69 micromolar without
SARS-CoV). Eventhough ACE2 is not a potential receptor for nicotine binding in
the healthy people, in COVID19 patients, it may exhibit better binding affinity
with the ACE2 receptor. In overall, nicotines strong preference for
ACE2-SARS-CoV-2 complex might drastically reduce the SARS-CoV-2 virulence by
intervening the ACE2 conserved residues interaction with the spike (S1) protein
of SARS-CoV-2.
Soft Affirmative Action and Minority Recruitment
Date: 2020-04-30 00:00:00
Tasks: Position

We study search, evaluation, and selection of candidates of unknown quality
for a position. We examine the effects of "soft" affirmative action policies
increasing the relative percentage of minority candidates in the candidate
pool. We show that, while meant to encourage minority hiring, such policies may
backfire if the evaluation of minority candidates is noisier than that of
non-minorities. This may occur even if minorities are at least as qualified and
as valuable as non-minorities. The results provide a possible explanation for
why certain soft affirmative action policies have proved counterproductive,
even in the absence of (implicit) biases.
Failure of monotonicity in epidemic models
Date: 2020-04-30 00:00:00

We discuss the failure of monotonicity properties for even simple
compartmental epidemic models, for the case where transmission rates are
non-constant. We also identify a special case in which monotonicity holds.
Massive viral replication and cytopathic effects in early COVID-19 pneumonia
Authors: L. Schifanella, J. L. Anderson, M. Galli, M. Corbellino, A. Lai, G. Wieking, B. Grzywacz, N. R. Klatt, A. T. Haase, T. W. Schacker
Date: 2020-04-30 00:00:00

SARS-CoV-2 is the cause of COVID-19 acute respiratory illness that like its predecessors, MERS and SARS, can be severe and fatal 1-4. By April of 2020, COVID-19 infections had become a worldwide pandemic with nearly 3 million infections and over 200,000 deaths. The relative contributions of virus replication and cytopathic effects or immunopathological host responses to the severe and fatal outcomes of COVID-19 lung infections have as yet to be determined. Here we show that SARS-CoV-2 replication and cytopathic effects in type II alveolar pneumocytes causes focal lung injury in an individual with no history of pulmonary symptoms. These findings point to the potential benefit of early effective antiviral treatment to prevent progression to severe and fatal COVID-19 pneumonia.
Non-line-of-sight imaging off a Phong surface through deep learning
Date: 2020-04-30 00:00:00
Tasks: SSIM

A deep learning based non-line-of-sight (NLOS) imaging system is developed to
image an occluded object off a scattering surface. The neural net is trained
using only handwritten digits, and yet exhibits capability to reconstruct
patterns distinct from the training set, including physical objects. It can
also reconstruct a cartoon video from its scattering patterns in real time,
demonstrating the robustness and generalization capability of the deep learning
based approach. Several scattering surfaces with varying degree of Lambertian
and specular contributions were examined experimentally; it is found that for a
Lambertian surface the structural similarity index (SSIM) of reconstructed
images is about 0.63, while the SSIM obtained from a scattering surface
possessing a specular component can be as high as 0.93. A forward model of
light transport was developed based on the Phong scattering model. Scattering
patterns from Phong surfaces with different degrees of specular contribution
were numerically simulated. It is found that a specular contribution of as
small as 5% can enhance the SSIM from 0.83 to 0.93, consistent with the results
from experimental data. Singular value spectra of the underlying transfer
matrix were calculated for various Phong surfaces. As the weight and the
shininess factor increase, i.e., the specular contribution increases, the
singular value spectrum broadens and the 50-dB bandwidth is increased by more
than 4X with a 10% specular contribution, which indicates that at the presence
of even a small amount of specular contribution the NLOS measurement can retain
significantly more singular value components, leading to higher reconstruction
fidelity. With an ordinary camera and incoherent light source, this work
enables a low-cost, real-time NLOS imaging system without the need of an
explicit physical model of the underlying light transport process.