pwc-archive/papers-with-abstracts huggingface dataset:
CommUnet: U-net decoder for convolutional codes in communication
Date: 2020-04-20 00:00:00
Tasks: Decoder, Denoising

In recent years, deep neural networks have played a major role solving
various challenges in two dimensional image processing.Fully Convolutional
Networks (FCN) such as U-net have been shown to be highly successful at
segmentation tasks for medical images analysis and denoising images taken in
dark venues. This paper harnesses this well-known deep neural network for the
channel decoding challenge recently proven to be suitable for deep neural
networks.Previous work have successfully managed to decode convolutional codes
using different architectures,such as Recurrent Neural Networks(RNN) and Fully
Connected Neural Networks(FCNN) with promising results.However,these approaches
are extremely costly in latency,computational resources and memory.This paper
shows that taking the approach used in two dimensional image processing,by
simple manipulation on the data in the preprocessing phase,achieves better
results in a Bit Error Rate(BER) measurement with a large discount on the
latency and the number of parameters required to maintain the neural decoder.
On the uncertainty of real-time predictions of epidemic growths: a COVID-19 case study for China and Italy
Date: 2020-04-20 00:00:00

While COVID-19 is rapidly propagating around the globe, the need for
providing real-time forecasts of the epidemics pushes fits of dynamical and
statistical models to available data beyond their capabilities. Here we focus
on statistical predictions of COVID-19 infections performed by fitting
asymptotic distributions to actual data. By taking as a case-study the epidemic
evolution of total COVID-19 infections in Chinese provinces and Italian
regions, we find that predictions are characterized by large uncertainties at
the early stages of the epidemic growth. Those uncertainties significantly
reduce after the epidemics peak is reached. Differences in the uncertainty of
the forecasts at a regional level can be used to highlight the delay in the
spread of the virus. Our results warn that long term extrapolation of epidemics
counts must be handled with extreme care as they crucially depend not only on
the quality of data, but also on the stage of the epidemics, due to the
intrinsically non-linear nature of the underlying dynamics. These results
suggest that real-time epidemiological projections should include wide
uncertainty ranges and urge for the needs of compiling high-quality datasets of
infections counts, including asymptomatic patients.
Wealth Effect on Portfolio Allocation in Incomplete Markets
Authors: Chenxu Li, Olivier Scaillet, Yiwen Shen
Date: 2020-04-21 00:00:00

We develop a novel five-component decomposition of optimal dynamic portfolio choice, which reveals the simultaneous impacts from market incompleteness and wealth-dependent utilities. Under the HARA utility and a nonrandom interest rate, we can explicitly solve for the optimal policy as a combination of a bond holding scheme and the corresponding simpler CRRA strategy. Under a stochastic volatility model estimated on US equity data, we use closed-form solution to demonstrate the sophisticated impacts from the wealth-dependent utilities, including cycle-dependence and hysteresis effect in optimal portfolio allocation, as well as a risk-return trade-off in investment performance.
Mathematical Modeling of the Spread of COVID-19 in Moscow and Russian Regions
Date: 2020-04-21 00:00:00

To model the spread of COVID-19 coronavirus in Russian regions and in Moscow,
a discrete logistic equation describing the increase in the number of cases is
used. To check the adequacy of the mathematical model, the simulation results
were compared with the spread of coronavirus in China, in a number of European
and Asian countries, and the United States. The parameters of the logistics
equation for Russia, Moscow and other large regions were determined in the
interval (01.03 - 08.04). A comparative analysis of growth rates of COVID-19
infected population for different countries and regions is presented. Various
scenarios of the spread of COVID-19 coronavirus in Moscow and in the regions of
Russia are considered. For each scenario, curves for the daily new cases and
graphs for the increase in the total number of cases were obtained, and the
dynamics of infection spread by day was studied. Peak times, epidemic periods,
the number of infected people at the peak and their growth were determined.
Impacts of Social Distancing Policies on Mobility and COVID-19 Case Growth in the US
Authors: Gregory A. Wellenius, Swapnil Vispute, Valeria Espinosa, Alex Fabrikant, Thomas C. Tsai, Jonathan Hennessy, Andrew Dai, Brian Williams, Krishna Gadepalli, Adam Boulanger, Adam Pearce, Chaitanya Kamath, Arran Schlosberg, Catherine Bendebury, Chinmoy Mandayam, Charlotte Stanton, Shailesh Bavadekar, Christopher Pluntke, Damien Desfontaines, Benjamin Jacobson, Zan Armstrong, Bryant Gipson, Royce Wilson, Andrew Widdowson, Katherine Chou, Andrew Oplinger, Tomer Shekel, Ashish K. Jha, Evgeniy Gabrilovich
Date: 2020-04-21 00:00:00

Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth 2 to 4 weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth 2 weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Authors: He Zhang, Liang Zhang, Ang Lin, Congcong Xu, Ziyu Li, Kaibo Liu, Boxiang Liu, Xiaopin Ma, Fanfan Zhao, Weiguo Yao, Hangwen Li, David H. Mathews, Yujian Zhang, Liang Huang
Date: 2020-04-21 00:00:00
Tasks: Protein Language Model, Sentence

Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine. Previous work showed that optimizing secondary structure stability lengthens mRNA half-life, which, together with optimal codons, increases protein expression. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage to improve mRNA efficiency. However, due to synonymous codons, the mRNA design space is prohibitively large, e.g., there are $\sim\!10^{632}$ mRNAs for the SARS-CoV-2 Spike protein, which poses insurmountable challenges to previous methods. Here we provide a surprisingly simple solution to this hard problem by reducing it to a classical problem in computational linguistics, where finding the optimal mRNA is akin to finding the most likely sentence among similar sounding alternatives. Our algorithm, named LinearDesign, takes only 11 minutes for the Spike protein, and can jointly optimize stability and codon usage. Experimentally, without chemical modification, our designs substantially improve mRNA half-life and protein expression in vitro, and dramatically increase antibody response by up to 23$\times$ in vivo, compared to the codon-optimized benchmark. Our work enables the exploration of highly stable and efficient designs that are previously unreachable and is a timely tool not only for vaccines but also for mRNA medicine encoding all therapeutic proteins (e.g., monoclonal antibodies and anti-cancer drugs).
Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications
Date: 2020-04-21 00:00:00

The primary cell surface receptor for SARS-CoV-2 is the
angiotensin-converting enzyme 2 (ACE2). Recently it has been noticed that the
viral Spike protein has an RGD motif, suggesting that cell surface integrins
may be co-receptors. We examined the sequences of ACE2 and integrins with the
Eukaryotic Linear Motif resource, ELM, and were presented with candidate short
linear motifs (SLiMs) in their short, unstructured, cytosolic tails with
potential roles in endocytosis, membrane dynamics, autophagy, cytoskeleton and
cell signalling. These SLiM candidates are highly conserved in vertebrates.
They suggest potential interactions with the AP2 mu2 subunit as well as I-BAR,
LC3, PDZ, PTB and SH2 domains found in signalling and regulatory proteins
present in epithelial lung cells. Several motifs overlap in the tail sequences,
suggesting that they may act as molecular switches, often involving tyrosine
phosphorylation status. Candidate LIR motifs are present in the tails of ACE2
and integrin beta3, suggesting that these proteins can directly recruit
autophagy components. We also noticed that the extracellular part of ACE2 has a
conserved MIDAS structural motif, which are commonly used by beta integrins for
ligand binding, potentially supporting the proposal that integrins and ACE2
share common ligands. The findings presented here identify several molecular
links and testable hypotheses that might help uncover the mechanisms of
SARS-CoV-2 attachment, entry and replication, and strengthen the possibility
that it might be possible to develop host-directed therapies to dampen the
efficiency of viral entry and hamper disease progression. The strong sequence
conservation means that these putative SLiMs are good candidates: Nevertheless,
SLiMs must always be validated by experimentation before they can be stated to
be functional.
Estimaci\'on del n\'umero de reproducci\'on de la epidemia COVID-19 en Culiac\'an Sinaloa, M\'exico
Date: 2020-04-21 00:00:00

Currently the COVID-19 epidemic is developing in the City of Culiac\'n
Sinaloa, Mexico, where up to April 20 of this year there have been 35 deaths
associated with this epidemic. The reproduction number $(R_0)$ of an epidemic
represents the average number of people infected by an infected person during
their period of infection. In this work we use the data published by the
Secretary of Health of the State of Sinaloa on the number of new infected cases
confirmed per day and we estimate that the value of $R_0$ is 1.562 with a 95%
confidence interval given by (1.401,1.742). We also estimate the mortality rate
among the confirmed cases, which turned out to be 16.8%.
  - - - - -
  Actualmente la epidemia COVID-19 se est\'a desarrollando en la Ciudad de
Culiac\'an Sinaloa, M\'exico, donde hasta el 20 de abril del presente a\~no han
ocurrido 35 decesos asociados con esta epidemia. El n\'umero de reproducci\'on
$(R_0)$ de una epidemia representa el n\'umero promedio de personas contagiadas
por una persona infectada durante su periodo de infecci\'on. En este trabajo
usamos los datos publicados por la Secretaria de Salud del Estado de Sinaloa
sobre el n\'umero de nuevos casos infectados confirmados por dia y estimamos
que el valor de $R_0$ es de 1.562 con un intervalo del 95% de confianza dado
por (1.401,1.742). Estimamos tambi\'en la tasa de mortalidad entre los casos
confirmados, la cual result\'o ser de 16.8%.
The Role of Redundant Bases and Shrinkage Functions in Image Denoising
Date: 2020-04-21 00:00:00
Tasks: Denoising, Image Denoising

Wavelet denoising is a classical and effective approach for reducing noise in
images and signals. Suggested in 1994, this approach is carried out by
rectifying the coefficients of a noisy image in the transform domain, using a
set of scalar shrinkage function (SFs). A plethora of papers deals with the
optimal shape of the SFs and the transform used, where it is known that
applying the SFs in redundant bases provides improved results. This paper
provides a complete picture of the interrelations between the transform used,
the optimal shrinkage functions, and the domains in which they are optimized.
In particular, we show that for subband optimization, where each SF is
optimized independently for a particular band, optimizing the SFs in the
spatial domain is always better than or equal to optimizing the SFs in the
transform domain. For redundant bases, we provide the expected denoising gain
we may achieve, relative to the unitary basis, as a function of the redundancy
rate.
Refining Understanding of Corporate Failure through a Topological Data Analysis Mapping of Altman's Z-Score Model
Authors: Wanling Qiu, Simon Rudkin, Pawel Dlotko
Date: 2020-04-21 00:00:00
Tasks: Topological Data Analysis

Corporate failure resonates widely leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Firms are represented as a point cloud in a five dimensional space, one axis for each predictor. Visualising that cloud using Ball Mapper reveals failing firms are not often neighbours. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy but actually sit in characteristic spaces where failure has not occurred.
Probabilistic analysis of masked loads with aggregated photovoltaic production
Date: 2020-04-21 00:00:00

In this paper we present a probabilistic analysis framework to estimate
behind-the-meter photovoltaic generation in real time. We develop a forward
model consisting of a spatiotemporal stochastic process that represents the
photovoltaic generation and a stochastic differential equation with jumps that
represents the demand. We employ this model to disaggregate the
behind-the-meter photovoltaic generation using net load and irradiance
measurements.
Optical coherence tomography imaging of evoked neural activity in sciatic nerve of rat
Authors: James Hope, Matthew Goodwin, Frederique Vanholsbeeck
Date: 2020-04-22 00:00:00

Significance: Imaging neural activity in myelinated tissue using optical coherence tomography (OCT) creates new possibilities for functional imaging in the peripheral and central nervous systems. Aim: To investigate changes in OCT images in response to evoked neural activity in sciatic nerve of rat in vitro. Approach: M-scans were obtained on peripheral nerves of rat using a swept source polarisation sensitive OCT system, while a nerve cuff acquired electrical neural recordings. From a total of 10 subjects: 3 had no stimulation (controls), 3 had paw stimulation, and 4 had nerve stimulation. Changes in the OCT signal intensity, phase retardation, phase, and frequency spectra were calculated for each subject and reference samples of a mirror and microspheres in solution. Results: Observed changes in intensity in 3 paw stimulation and 2 nerve stimulation subjects and changes in frequency spectra amplitude in 2 paw stimulation subjects were above the reference noise level and were temporally consistent with osmotic swelling from ion currents during neural activity. Conclusion: Light scattering changes produced by osmotic swelling, which have previously been characterised in squid and crab nerve, are also thought to occur in myelinated fibres on a scale which is detectable using OCT
LAI-Net: Local-Ancestry Inference with Neural Networks
Authors: Daniel Mas Montserrat, Carlos Bustamante, Alexander Ioannidis
Date: 2020-04-22 00:00:00

Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in DNA sequence analysis with applications extending from polygenic risk scores (used to predict traits in embryos and disease risk in adults) to genome-wide association studies, and from pharmacogenomics to inference of human population history. While many LAI methods have been developed, advances in computing hardware (GPUs) combined with machine learning techniques, such as neural networks, are enabling the development of new methods that are fast, robust and easily shared and stored. In this paper we develop the first neural network based LAI method, named LAI-Net, providing competitive accuracy with state-of-the-art methods and robustness to missing or noisy data, while having a small number of layers.
Logistic and $\theta$-logistic models in population dynamics: General analysis and exact results
Date: 2020-05-26 00:00:00

In the present paper we provide the closed form of the path-like solutions
for the logistic and $\theta$-logistic stochastic differential equations, along
with the exact expressions of both their probability density functions and
their moments. We simulate in addition a few typical sample trajectories, and
we provide a few examples of numerical computation of the said closed formulas
at different noise intensities: this shows in particular that an increasing
randomness - while making the process more unpredictable - asymptotically tends
to suppress in average the logistic growth. These main results are preceded by
a discussion of the noiseless, deterministic versions of these models: a
prologue which turns out to be instrumental - on the basis of a few simplified
but functional hypotheses - to frame the logistic and $\theta$-logistic
equations in a unified context, within which also the Gompertz model emerges
from an anomalous scaling.
OUTBREAK: A user-friendly georeferencing online tool for disease surveillance
Date: 2020-04-22 00:00:00

The current COVID-19 pandemic has already claimed more than 100,000 victims
and it will cause more deaths in the coming months. Tools that can track the
number and locations of cases are critical for surveillance and can help in
making policy decisions for controlling the outbreak. The current surveillance
web-based dashboards run on proprietary platforms, which are often expensive
and require specific computational knowledge. We present a new tool (OUTBREAK)
for studying and visualizing epidemiological data. It permits even
non-specialist users to input data most conveniently and track outbreaks in
real-time. This tool has the potential to guide and help health authorities to
intervene and minimize the effects of the outbreaks. It is freely available at
http://outbreak.sysbio.tools/.
TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization
Authors: Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Joan Bordoy, Andrea Gabbrielli, Junli Liang
Date: 2020-04-22 00:00:00

This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at bypassing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the $\ell_1$-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement.
Enhancement of Particle Image Velocimetry Images
Date: 2020-04-22 00:00:00

Particle Image Velocimetry (PIV) is a method to visualize the flows and
quantitatively map the flows. It is used to obtain the instantaneous velocity,
vorticity, divergence, shear in fluids, etc. Laser Doppler velocimetry and hot
wire anemometry are other techniques that are used to measure the flow. The
main difference between PIV and those techniques is that PIV produces
two-dimensional or three-dimensional vector fields. PIV uses two sequences of
images (frames) and the frames are split into a large number of interrogation
windows. The displacement vector is calculated for each window with the help of
auto-correlation or cross-correlation techniques. Missing data during these
processes leads to inaccurate results. To overcome this, preprocessing
techniques such as histogram equalization and intensity clipping are used to
enhance the PIV images to obtain accurate results.
Towards Efficient Hazard Identification in the Concept Phase of Driverless Vehicle Development
Date: 2020-04-22 00:00:00

The complex functional structure of driverless vehicles induces a multitude of potential malfunctions. Established approaches for a systematic hazard identification generate individual potentially hazardous scenarios for each identified malfunction. This leads to inefficiencies in a purely expert-based hazard analysis process, as each of the many scenarios has to be examined individually. In this contribution, we propose an adaptation of the strategy for hazard identification for the development of automated vehicles. Instead of focusing on malfunctions, we base our process on deviations from desired vehicle behavior in selected operational scenarios analyzed in the concept phase. By evaluating externally observable deviations from a desired behavior, we encapsulate individual malfunctions and reduce the amount of generated potentially hazardous scenarios. After introducing our hazard identification strategy, we illustrate its application on one of the operational scenarios used in the research project UNICAR$agil$.
Performance Analysis of Novel Direct Access Schemes for LEO Satellites Based IoT Network
Date: 2020-04-22 00:00:00
Tasks: Diversity

This paper analyzes the performance of low earth orbit (LEO) satellites based
internet-of-things (IoT) network where each IoT node makes use of multiple
satellites to communicate with the ground station (GS). In this work, we
consider fixed and variable gain amplify-and-forward (AF) relaying protocol at
each satellite where the received signal from each IoT node is amplified before
transmitting to the terrestrial GS for data processing. To analyze the
performance of this novel LEO satellites based direct access architecture, the
closed-form expressions for outage probability are derived considering two
combining schemes at the GS: (i) selection combining; (ii) maximal ratio
combining. Further, to gain more insights for diversity order and coding gain,
asymptotic outage probability analysis at high SNR for both schemes is also
performed. Finally, simulation results are presented to validate the analytical
results derived and also to develop several interesting insights into the
system performance.