pwc-archive/papers-with-abstracts huggingface dataset:
Calculating Germinal Centre Reactions
Date: 2019-07-28 00:00:00

Germinal centres are anatomically defined lymphoid organ structures that
mediate B cell affinity maturation and affect the quality of humoral immune
responses. Mathematical models based on differential equations or agent-based
simulations have been widely used to deepen our understanding of the cellular
and molecular processes characterizing these complex dynamic systems. Along
with experimental studies, these tools have provided insights into the
spatio-temporal behavior of B cells in germinal center reactions and the
mechanisms underlying their clonal selection and cellular differentiation. More
recently, mathematical models have been used to define key parameters that
influence the quality of humoral vaccine responses such as vaccine composition
and vaccination schedule. Here, we review current mathematical and
computational models of the GC response, highlight interesting model features
and discuss how they helped to improve our understanding of B cell responses
specifically in immunization settings.
Two Efficient Beamformers for Secure Precise Jamming and Communication with Phase Alignment
Date: 2019-07-31 00:00:00

To achieve a better effect of interference on eavesdropper with an enhanced
security, a secure precise jamming (PJ) and communication (SPJC) is proposed
and its basic idea is to force the transmit energy of artificial noise (AN) and
confidential message into the neighborhoods of Eve and Bob by using random
subcarrier selection (RSS), directional modulation, and beamforming under phase
alignment (PA) constraint (PAC). Here, we propose two high-performance
beamforming schemes: minimum transmit power (Min-TP) and minimum regularized
transmit power (Min-RTP) to achieve SPJC under PAC and orthogonal constraint
(OC), where OC means that AN and CM are projected onto the null-spaces of the
desired and eavesdropping channels, respectively. Simulation results show that
the proposed Min-TP and Min-RTP methods perform much better than existing equal
amplitude (EA) method in terms of both bit-error-rate (BER) and secrecy rate
(SR) at medium and high signal-to-noise ratio regions. The SR performance
difference between the proposed two methods becomes trivial as the number of
transmit antennas approaches large-scale. More importantly, we also find the
fact that all three schemes including EA, Min-TP, and Min-RTP can form two main
peaks of AN and CM around Eve and Bob, respectively. This achieves both PJ and
secure precise wireless transmission (SPWT), called SPJC.
Data-driven computation of invariant sets of discrete time-invariant black-box systems
Authors: Zheming Wang, Raphaël M. Jungers
Date: 2019-07-28 00:00:00

We consider the problem of computing the maximal invariant set of discrete-time black-box nonlinear systems without analytic dynamical models. Under the assumption that the system is asymptotically stable, the maximal invariant set coincides with the domain of attraction. A data-driven framework relying on the observation of trajectories is proposed to compute almost-invariant sets, which are invariant almost everywhere except a small subset. Based on these observations, scenario optimization problems are formulated and solved. We show that probabilistic invariance guarantees on the almost-invariant sets can be established. To get explicit expressions of such sets, a set identification procedure is designed with a verification step that provides inner and outer approximations in a probabilistic sense. The proposed data-driven framework is illustrated by several numerical examples.
Effects of resource competition on evolution and adaptive radiation
Authors: Sergei V. Koniakhin
Date: 2019-07-28 00:00:00

The entanglement of population dynamics, evolution, and adaptive radiation for species competing for resources is studied. For resource harvesting, we modify the model used in Ref. Phys. Rev. Lett. 118 048103 and introduce new resource contest principles. We realistically implement the effects of beneficial and deleterious mutations on the coefficients in the equations governing the population dynamics and consider the emergence of reproductive isolation. The proposed model is shown to be in agreement with the competitive exclusion principle and no vacant niche principle. We establish a mechanism that contributes to preventing the accumulation of irreversible deleterious mutations: competition between recently diverged species/subpopulations. The proposed model is applicable for descriptions of more complex systems. In case of many constants in time resources, one observes very rapid specialization, a feature not reproducible by the common model.
Taxable Stock Trading with Deep Reinforcement Learning
Authors: Shan Huang
Date: 2019-07-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance could induce more than 62% loss on the average portfolio returns, implying that taxes should be embedded in the environment of continuous stock trading on AI platforms.
On Norm-Based Estimations for Domains of Attraction in Nonlinear Time-Delay Systems
Date: 2020-05-19 00:00:00

For nonlinear time-delay systems, domains of attraction are rarely studied
despite their importance for technological applications. The present paper
provides methodological hints for the determination of an upper bound on the
radius of attraction by numerical means. Thereby, the respective Banach space
for initial functions has to be selected and primary initial functions have to
be chosen. The latter are used in time-forward simulations to determine a first
upper bound on the radius of attraction. Thereafter, this upper bound is
refined by secondary initial functions, which result a posteriori from the
preceding simulations. Additionally, a bifurcation analysis should be
undertaken. This analysis results in a possible improvement of the previous
estimation. An example of a time-delayed swing equation demonstrates the
various aspects.
Fast Diffusion Inhibits Disease Outbreaks
Date: 2019-07-29 00:00:00
Tasks: Math

We show that the basic reproduction number of an SIS patch model with
standard incidence is either strictly decreasing and strictly convex with
respect to the diffusion coefficient of infected subpopulation if the patch
reproduction numbers of at least two patches in isolation are distinct or
constant otherwise. Biologically, it means that fast diffusion of the infected
people reduces the risk of infection. This completely solves and generalizes a
conjecture by Allen et al. ({\it SIAM J Appl Math}, 67: 1283-1309, 2007).
Furthermore, a substantially improved lower bound on the multipatch
reproduction number, a generalized monotone result on the spectral bound the
Jacobian matrix of the model system at the disease-free equilibrium, and the
limiting endemic equilibrium are obtained. The approach and results can be
applied to a class of epidemic patch models where only one class of infected
compartments migrate between patches and one transmission route is involved.
Jamming-assisted Proactive Eavesdropping over Two Suspicious Communication Links
Date: 2019-07-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper studies a new and challenging wireless surveillance problem where
a legitimate monitor attempts to eavesdrop two suspicious communication links
simultaneously. To facilitate concurrent eavesdropping, our multi-antenna
legitimate monitor employs a proactive eavesdropping via jamming approach, by
selectively jamming suspicious receivers to lower the transmission rates of the
target links. In particular, we are interested in characterizing the achievable
eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver
case, by optimizing the legitimate monitor's jamming transmit covariance matrix
subject to its power budget. As the monitor cannot hear more than what
suspicious links transmit, the achievable eavesdropping rate region is
essentially the intersection of the achievable rate region for the two
suspicious links and that for the two eavesdropping links. The former region
can be purposely altered by the monitor's jamming transmit covariance matrix,
whereas the latter region is fixed when the MMSE receiver is employed.
Therefore, we first analytically characterize the achievable rate region for
the two suspicious links via optimizing the jamming transmit covariance matrix
and then obtain the achievable eavesdropping rate region for the MMSE receiver
case. Furthermore, we also extend our study to the MMSE with successive
interference cancellation (MMSE-SIC) receiver case and characterize the
corresponding achievable eavesdropping rate region by jointly optimizing the
time-sharing factor between different decoding orders. Finally, numerical
results are provided to corroborate our analysis and examine the eavesdropping
performance.
Weighted Spectral Efficiency Optimization for Hybrid Beamforming in Multiuser Massive MIMO-OFDM Systems
Date: 2019-07-29 00:00:00

In this paper, we consider hybrid beamforming designs for multiuser massive
multiple-input multiple-output (MIMO)-orthogonal frequency division
multiplexing (OFDM) systems. Aiming at maximizing the weighted spectral
efficiency, we propose one alternating maximization framework where the analog
precoding is optimized by Riemannian manifold optimization. If the digital
precoding is optimized by a locally optimal algorithm, we obtain a locally
optimal alternating maximization algorithm. In contrast, if we use a weighted
minimum mean square error (MMSE)-based iterative algorithm for digital
precoding, we obtain a suboptimal alternating maximization algorithm with
reduced complexity in each iteration. By characterizing the upper bound of the
weighted arithmetic and geometric means of mean square errors (MSEs), it is
shown that the two alternating maximization algorithms have similar performance
when the user specific weights do not have big differences. Verified by
numerical results, the performance gap between the two alternating maximization
algorithms becomes large when the ratio of the maximal and minimal weights
among users is very large. Moreover, we also propose a low-complexity
closed-form method without iterations. It employs matrix decomposition for the
analog beamforming and weighted MMSE for the digital beamforming. Although it
is not supposed to maximize the weighted spectral efficiency, it exhibits small
performance deterioration compared to the two iterative alternating
maximization algorithms and it qualifies as a good initialization for iterative
algorithms, saving thereby iterations.
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems
Date: 2019-07-29 00:00:00

A predictive triggering (PT) framework for the distributed control of
resource constrained multi-agent systems is proposed. By predicting future
communication demands and deriving a probabilistic priority measure, the PT
framework is able to allocate limited communication resources in advance. The
framework is evaluated through simulations of a cooperative adaptive cruise
control system and experiments on multi-agent cart-pole systems. The results of
these studies show its effectiveness over other event-triggered designs at
reducing network utilization, while also improving the control error of the
system.
Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries
Date: 2019-07-29 00:00:00

In this study, we combine a standardized acquisition protocol with image
analysis algorithms to investigate if it is possible to automatically detect
maternal risk factors without a trained sonographer. The standardized
acquisition protocol can be taught to any health care worker within two hours.
This protocol was acquired from 280 pregnant women at St.\ Luke's Catholic
Hospital, Wolisso, Ethiopia. A VGG-like network was used to perform a frame
classification for each frame within the acquired ultrasound data. This frame
classification was used to automatically determine the number of fetuses and
the fetal presentation. A U-net was trained to measure the fetal head
circumference in all frames in which the VGG-like network detected a fetal
head. This head circumference was used to estimate the gestational age. The
results show that it possible automatically determine gestational age and
determine fetal presentation and the potential to detect twin pregnancies using
the standardized acquisition protocol.
Conditioning Convolutional Segmentation Architectures with Non-Imaging Data
Date: 2019-07-29 00:00:00
Tasks: Segmentation

We compare two conditioning mechanisms based on concatenation and
feature-wise modulation to integrate non-imaging information into convolutional
neural networks for segmentation of anatomical structures. As a
proof-of-concept we provide the distribution of class labels obtained from
ground truth masks to ensure strong correlation between the conditioning data
and the segmentation maps. We evaluate the methods on the ACDC dataset, and
show that conditioning with non-imaging data improves performance of the
segmentation networks. We observed conditioning the U-Net architectures was
challenging, where no method gave significant improvement. However, the same
architecture without skip connections outperforms the baseline with
feature-wise modulation, and the relative performance increases as the training
size decreases.
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations
Date: 2019-07-29 00:00:00
Tasks: Multi-class Classification, Multi-Task Learning

We propose a learning method well-suited to infer the presence of
Tuberculosis (TB) manifestations on Computer Tomography (CT) scans mimicking
the radiologist reports. Latent features are extracted from the CT volumes
employing the V-Net encoder and those are the input to a Feed-Forward Neural
Network (FNN) for multi-class classification. To overtake the issues (e.g.,
exploding/vanishing gradients, lack of sensibility) that normally appear when
training deep 3D models with datasets of limited size and composed of large
volumes, our proposal employs: 1) At the network architecture level, the scaled
exponential linear unit (SELU) activation which allows the network
self-normalization, and 2) at the learning phase, multi-task learning with a
loss function weighted by the task homoscedastic uncertainty. The results
achieve F1-scores close to or above 0.9 for the detection of TB lesions and a
Root Mean Square Error of 1.16 for the number of nodules.
Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection
Date: 2019-07-29 00:00:00
Tasks: Lesion Detection, Segmentation

We propose and evaluate a novel method for automatically detecting clinically
significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging
(bpMRI). Prostate zones play an important role in the assessment of prostate
cancer on MRI. We hypothesize that the inclusion of zonal information can
improve the performance of a deep learning based csPCa lesion detection model.
However, segmentation of prostate zones is challenging and therefore
deterministic models are inaccurate. Hence, we investigated probabilistic zonal
segmentation. Our baseline detection model is a 2DUNet trained to produce a
csPCa heatmap followed by a 3D detector. We experimented with the integration
of zonal prior information by fusing the output of an anisotropic 3DUNet
trained to produce either a deterministic or probabilistic map for each
prostate zone. We also investigate the effect of early or late fusion on csPCa
detection. All methods were trained and tested on 848 bpMRI. The results show
that fusing zonal prior knowledge improves the baseline detection model with a
preference for probabilistic over deterministic zonal segmentation.
Killer Technologies: the destructive creation in the technical change
Date: 2019-07-29 00:00:00
Tasks: Management

Killer technology is a radical innovation, based on new products and/or
processes, that with high technical and/or economic performance destroys the
usage value of established techniques previously sold and used. Killer
technology is a new concept in economics of innovation that may be useful for
bringing a new perspective to explain and generalize the behavior and
characteristics of innovations that generate a destructive creation for
sustaining technical change. To explore the behavior of killer technologies, a
simple model is proposed to analyze and predict how killer technologies destroy
and substitute established technologies. Empirical evidence of this theoretical
framework is based on historical data on the evolution of some example
technologies. Theoretical framework and empirical evidence hint at general
properties of the behavior of killer technologies to explain corporate,
industrial, economic and social change and to support best practices for
technology management of firms and innovation policy of nations. Overall, then,
the proposed theoretical framework can lay a foundation for the development of
more sophisticated concepts to explain the behavior of vital technologies that
generate technological and industrial change in society.
Robust reconstruction of cardiac T1 maps using RNNs
Date: 2019-07-29 00:00:00

Cardiac magnetic resonance parametric T1 maps are typically reconstructed
using non-linear fitting. However this method has limitations due to the high
computational cost and robustness. In this study, a recurrent neural network
(RNN) is proposed for the robust and fast reconstruction of cardiac T1 maps.
Multiple Subordinated Modeling of Asset Returns
Date: 2019-07-29 00:00:00

Subordination is an often used stochastic process in modeling asset prices.
Subordinated Levy price processes and local volatility price processes are now
the main tools in modern dynamic asset pricing theory. In this paper, we
introduce the theory of multiple internally embedded financial time-clocks
motivated by behavioral finance. To be consistent with dynamic asset pricing
theory and option pricing, as suggested by behavioral finance, the investors'
view is considered by introducing an intrinsic time process which we refer to
as a behavioral subordinator. The process is subordinated to the Brownian
motion process in the well-known log-normal model, resulting in a new log-price
process. The number of embedded subordinations results in a new parameter that
must be estimated and this parameter is as important as the mean and variance
of asset returns. We describe new distributions, demonstrating how they can be
applied to modeling the tail behavior of stock market returns. We apply the
proposed models to modeling S&P 500 returns, treating the CBOE Volatility Index
as intrinsic time change and the CBOE Volatility-of-Volatility Index as the
volatility subordinator. We find that these volatility indexes are not proper
time-change subordinators in modeling the returns of the S&P 500.
5-GHz Antisymmetric Mode Acoustic Delay Lines in Lithium Niobate Thin Film
Date: 2019-10-12 00:00:00

We present the first group of acoustic delay lines (ADLs) at 5 GHz, using the
first-order antisymmetric (A1) mode in Z-cut lithium niobate thin films. The
demonstrated ADLs significantly surpass the operation frequency of the previous
works with similar feature sizes, because of its simultaneously fast phase
velocity, large coupling coefficient, and low-loss. In this work, the
propagation characteristics of the A1 mode in lithium niobate is analytically
modeled and validated with finite element analysis. The design space of A1 ADLs
is then investigated, including both the fundamental design parameters and
those introduced from the practical implementation. The implemented ADLs at 5
GHz show a minimum insertion loss of 7.94 dB, an average IL of 9.1 dB, and a
fractional bandwidth around 4%, with delays ranging between 15 ns to 109 ns and
the center frequencies between 4.5 GHz and 5.25 GHz. The propagation
characteristics of A1 mode acoustic waves have also been extracted for the
first time. The A1 ADL platform can potentially enable wide-band high-frequency
passive signal processing functions for future 5G applications in the sub-6 GHz
spectrum bands.
Autoencoder for Interconnect's Bandwidth Relaxation in Large Scale MIMO-OFDM Processing
Date: 2019-09-19 00:00:00
Tasks: Decoder

Deep learning is playing an instrumental role in the design of the next
generation of communication systems. In this letter, we address the massive
MIMO interconnect's bandwidth constraint relaxation using autoencoders. The
autoencoder is trained to learn the received signal structure so that a low
dimension latent variable is transferred as opposed to the original high
dimension signal. For an efficient implementation, the approach suggests to
separately deploy the autoencoder components, namely the encoder and the
decoder, in massive MIMO radio head and central processing units respectively.
The simulation results show that one can relax the interconnect's bandwidth by
a factor of up to 16 with non-substantial performance degradation of the
centralized and the decentralized processing. Fortunately, such a loss can be
compensated by running few extra iterations in the detection process which
renders the autoencoder-iterative detection an interconnect's bandwidth and
computational complexity design trade-off.
A procedure for loss-optimising default definitions across simulated credit risk scenarios
Authors: Arno Botha, Conrad Beyers, Pieter de Villiers
Date: 2019-07-29 00:00:00

A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears, can also be evaluated using this optimisation procedure. Furthermore, a simulation study is performed in testing the procedure from `first principles' across a wide range of credit risk scenarios. Specifically, three probabilistic techniques are used to generate cash flows, while the parameters of each are varied, as part of the simulation study. The results show that loss minima can exist for a select range of credit risk profiles, which suggests that the loss optimisation of default thresholds can become a viable practice. The default decision is therefore framed anew as an optimisation problem in choosing a default threshold that is neither too early nor too late in loan life. These results also challenges current practices wherein default is pragmatically defined as `90 days past due', with little objective evidence for its overall suitability or financial impact, at least beyond flawed roll rate analyses or a regulator's decree.
Fast and Robust 3-D Sound Source Localization with DSVD-PHAT
Date: 2019-07-29 00:00:00
Tasks: Sound Source Localization

This paper introduces a variant of the Singular Value Decomposition with
Phase Transform (SVD-PHAT), named Difference SVD-PHAT (DSVD-PHAT), to achieve
robust Sound Source Localization (SSL) in noisy conditions. Experiments are
performed on a Baxter robot with a four-microphone planar array mounted on its
head. Results show that this method offers similar robustness to noise as the
state-of-the-art Multiple Signal Classification based on Generalized Singular
Value Decomposition (GSVD-MUSIC) method, and considerably reduces the
computational load by a factor of 250. This performance gain thus makes
DSVD-PHAT appealing for real-time application on robots with limited on-board
computing power.