pwc-archive/papers-with-abstracts huggingface dataset:
Universal Basic Income: The Last Bullet in the Darkness
Authors: Mohammad Rasoolinejad
Date: 2019-10-12 00:00:00

Universal Basic Income (UBI) has recently been gaining traction. Arguments exist on both sides in favor of and against it. Like any other financial tool, UBI can be useful if used with discretion. This paper seeks to clarify how UBI affects the economy, including how it can be beneficial. The key point is to regulate the rate of UBI based on the inflation rate. This should be done by an independent institution from the executive branch of the government. If implemented correctly, UBI can add a powerful tool to the Federal Reserve toolkit. UBI can be used to reintroduce inflation to the countries which suffer long-lasting deflationary environment. UBI has the potential to decrease the wealth disparity, decrease the national debt, increase productivity, and increase comparative advantage of the economy. UBI also can substitute the current welfare systems because of its transparency and efficiency. This article focuses more on the United States, but similar ideas can be implemented in other developed nations.
Dynamics of bacterial populations under the feast-famine cycles
Date: 2020-03-10 00:00:00

Bacterial populations in natural conditions are expected to experience
stochastic environmental fluctuations, and in addition, environments are
affected by bacterial activities since they consume substrates and excrete
various chemicals. We here study possible outcomes of population dynamics and
evolution under the repeated cycle of substrate-rich conditions and starvation,
called the "feast-famine cycle", by a simple stochastic model with the
trade-off relationship between the growth rate and the growth yield or the
death rate. In the model, the feast (substrate-rich) period is led by a
stochastic substrate addition event, while the famine (starvation) period is
evoked because bacteria use the supplied substrate. Under the repeated
feast-famine cycle, the bacterial population tends to increase the growth rate,
even though that tends to decrease the total population size due to the
trade-off. Analysis of the model shows that the ratio between the growth rate
and the death rate becomes the effective fitness of the population. Hence, the
functional form of the trade-off between the growth and death rate determines
if the bacterial population eventually goes extinct as an evolutionary
consequence. We then show that the increase of the added substrate in the feast
period can drive the extinction faster. Overall, the model sheds light on
non-trivial possible outcomes under repeated feast-famine cycles.
The space of tree-based phylogenetic networks
Date: 2019-10-13 00:00:00

Phylogenetic networks are generalizations of phylogenetic trees that allow
the representation of reticulation events such as horizontal gene transfer or
hybridization, and can also represent uncertainty in inference. A subclass of
these, tree-based phylogenetic networks, have been introduced to capture the
extent to which reticulate evolution nevertheless broadly follows tree-like
patterns. Several important operations that change a general phylogenetic
network have been developed in recent years, and are important for allowing
algorithms to move around spaces of networks; a vital ingredient in finding an
optimal network given some biological data. A key such operation is the Nearest
Neighbor Interchange, or NNI. While it is already known that the space of
unrooted phylogenetic networks is connected under NNI, it has been unclear
whether this also holds for the subspace of tree-based networks. In this paper
we show that the space of unrooted tree-based phylogenetic networks is indeed
connected under the NNI operation. We do so by explicitly showing how to get
from one such network to another one without losing tree-basedness along the
way. Moreover, we introduce some new concepts, for instance ``shoat networks'',
and derive some interesting aspects concerning tree-basedness. Last, we use our
results to derive an upper bound on the size of the space of tree-based
networks.
Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition
Date: 2019-10-14 00:00:00
Tasks: Bayesian Inference, Hippocampus, Simultaneous Localization and Mapping

Fourier-like summation of several grid cell modules with different spatial
frequencies in the medial entorhinal cortex (MEC) has long been proposed to
form the contours of place firing fields. Recent experiments largely, but not
completely, support this theory. Place fields are obviously expanded by
inactivation of dorsal MEC, which fits the hypothesis. However, contrary to the
prediction, inactivation of ventral MEC is also weakly broaden the spatial
place firing patterns. In this study, we derive the model from grid spatial
frequencies represented by Gaussian profiles to a 1D place field by Bayesian
inference, and further provide completely theoretical explanations for
expansion of place fields and predictions for alignments of grid components. To
understand the information transform across between neocortex, entorhinal
cortex, and hippocampus, we propose spatial memory indexing theory from
hippocampal indexing theory to investigate how neural dynamics work in the
entorhinal-hippocampal circuit. The inputs of place cells in CA3 are converged
from three grid modules with different grid spacings layer II of MEC by
Bayesian mechanism. We resort to the robot system to test Fourier hypothesis
and spatial memory indexing theory, and validate our proposed
entorhinal-hippocampal model. And then we demonstrate its cognitive mapping
capability on the KITTI odometry benchmark dataset. Results suggest that our
model provides a rational theoretical explanation for the biological
experimental results. Results also show that the proposed model is robust for
simultaneous localization and mapping (SLAM) in the large-scale environment.
Our proposed model theoretically supports for Fourier hypothesis in a general
Bayesian mechanism, which may pertain to other neural systems in addition to
spatial cognition.
Option Pricing with Mixed Levy Subordinated Price Process and Implied Probability Weighting Function
Date: 2020-04-21 00:00:00
Tasks: Sensitivity

It is essential to incorporate the impact of investor behavior when modeling
the dynamics of asset returns. In this paper, we reconcile behavioral finance
and rational finance by incorporating investor behavior within the framework of
dynamic asset pricing theory. To include the views of investors, we employ the
method of subordination which has been proposed in the literature by including
business (intrinsic, market) time. We define a mixed Levy subordinated model by
adding a single subordinated Levy process to the well-known log-normal model,
resulting in a new log-price process. We apply the proposed models to study the
behavioral finance notion of "greed and fear" disposition from the perspective
of rational dynamic asset pricing theory. The greedy or fearful disposition of
option traders is studied using the shape of the probability weighting
function. We then derive the implied probability weighting function for the
fear and greed deposition of option traders in comparison to spot traders. Our
result shows the diminishing sensitivity of option traders. Diminishing
sensitivity results in option traders overweighting the probability of big
losses in comparison to spot traders.
Hybrid Beamforming for Terahertz Multi-Carrier Systems over Frequency Selective Fading
Authors: Hang Yuan, Nan Yang, Kai Yang, Chong Han, Jianping An
Date: 2019-10-14 00:00:00

We propose novel hybrid beamforming schemes for the terahertz (THz) wireless system where a multi-antenna base station (BS) communicates with a multi-antenna user over frequency selective fading. Here, we assume that the BS employs sub-connected hybrid beamforming and multi-carrier modulation to deliver ultra high data rate. We consider a three-dimensional wideband THz channel by incorporating the joint effect of molecular absorption, high sparsity, and multi-path fading, and consider the carrier frequency offset in multi-carrier systems. With this model, we first propose a two-stage wideband hybrid beamforming scheme which includes a beamsteering codebook searching algorithm for analog beamforming and a regularized channel inversion method for digital beamforming. We then propose a novel wideband hybrid beamforming scheme with two digital beamformers. In this scheme, an additional digital beamformer is developed to compensate for the performance loss caused by the constant-amplitude hardware constraints and the difference of channel matrices among subcarriers. Furthermore, we consider imperfect channel state information (CSI) and propose a probabilistic robust hybrid beamforming scheme to combat channel estimation errors. Numerical results demonstrate the benefits of our proposed schemes for the sake of practical implementation, especially considering its high spectral efficiency, low complexity, and robustness against imperfect CSI.
A BSDE-based approach for the optimal reinsurance problem under partial information
Authors: Matteo Brachetta, Claudia Ceci
Date: 2019-10-14 00:00:00

We investigate the optimal reinsurance problem under the criterion of maximizing the expected utility of terminal wealth when the insurance company has restricted information on the loss process. We propose a risk model with claim arrival intensity and claim sizes distribution affected by an unobservable environmental stochastic factor. By filtering techniques (with marked point process observations), we reduce the original problem to an equivalent stochastic control problem under full information. Since the classical Hamilton-Jacobi-Bellman approach does not apply, due to the infinite dimensionality of the filter, we choose an alternative approach based on Backward Stochastic Differential Equations (BSDEs). Precisely, we characterize the value process and the optimal reinsurance strategy in terms of the unique solution to a BSDE driven by a marked point process.
Image watermarking and fusion based on Fourier single-pixel imaging with weighed light source
Date: 2019-10-11 00:00:00

In previous single-pixel imaging systems, the light source was generally idle
with respect to time. Here, we propose a novel image fusion and visible
watermarking scheme based on Fourier single-pixel imaging (FSPI) with a
multiplexed time-varying (TV) signal, which is generated by the watermark
pattern hidden in the light source. We call this scheme as TV-FSPI. With
TV-FSPI, we can realize high-quality visible image watermarking, encrypted
image watermarking and full-color visible image watermarking. We also discuss
the extension to invisible watermarking based on TV-FSPI. Furthermore, we don't
have to recode illumination patterns, because TV-FSPI can be extended to
existing mainstream illumination patterns, such as random illumination mode and
Hadamard illumination mode. Thus TV-FSPI has the potential to be used in
single-pixel broadcasting system and multi-spectral single-pixel imaging
system.
TbCAPs: A ToolBox for Co-Activation Pattern Analysis
Date: 2019-10-14 00:00:00

Functional magnetic resonance imaging provides rich spatio-temporal data of
human brain activity during task and rest. Many recent efforts have focussed on
characterising dynamics of brain activity. One notable instance is
co-activation pattern (CAP) analysis, a frame-wise analytical approach that
disentangles the different functional brain networks interacting with a
user-defined seed region. While promising applications in various clinical
settings have been demonstrated, there is not yet any centralised, publicly
accessible resource to facilitate the deployment of the technique.
  Here, we release a working version of TbCAPs, a new toolbox for CAP analysis,
which includes all steps of the analytical pipeline, introduces new
methodological developments that build on already existing concepts, and
enables a facilitated inspection of CAPs and resulting metrics of brain
dynamics. The toolbox is available on a public academic repository
(https://c4science.ch/source/CAP_Toolbox.git).
  In addition, to illustrate the feasibility and usefulness of our pipeline, we
describe an application to the study of human cognition. CAPs are constructed
from resting-state fMRI using as seed the right dorsolateral prefrontal cortex,
and, in a separate sample, we successfully predict a behavioural measure of
continuous attentional performance from the metrics of CAP dynamics (R=0.59).
Effect of Harvest on MTE Calculated by Single Step Process for Stochastic Population Model Under Allee Effect
Date: 2019-10-14 00:00:00

In this study, first we expand the cubic population model under Allee effect
with a quadratic harvest function that represents harvest effect. Then, using
four reaction equations representing the micro-interactions within the
population under the influence of demographic noise and harvest, we obtain the
mean field equations containing the effect of the harvest from the solution of
the master equation. In this way, a relationship is established between the
micro and macro parameters of the population. As a result, we calculate Mean
Time to Extinction (MTE) by using WKB approximation for single step processes
and observe the effect of harvesting.
Phase separation and scaling in correlation structures of financial markets
Authors: Anirban Chakraborti, Hrishidev, Kiran Sharma, Hirdesh K. Pharasi
Date: 2019-10-10 00:00:00

Financial markets, being spectacular examples of complex systems, display rich correlation structures among price returns of different assets. The correlation structures change drastically, akin to phase transitions in physical phenomena, as do the influential stocks (leaders) and sectors (communities), during market events like crashes. It is crucial to detect their signatures for timely intervention or prevention. Here we use eigenvalue decomposition and eigen-entropy, computed from eigen-centralities of different stocks in the cross-correlation matrix, to extract information about the disorder in the market. We construct a `phase space', where different market events (bubbles, crashes, etc.) undergo phase separation and display order-disorder transitions. An entropy functional exhibits scaling behavior. We propose a generic indicator that facilitates the continuous monitoring of the internal structure of the market -- important for managing risk and stress-testing the financial system. Our methodology would help in understanding and foreseeing tipping points or fluctuation patterns in complex systems.
Bistable Dynamics and Hopf Bifurcation in a Refined Model of Early Stage HIV Infection
Date: 2019-10-14 00:00:00

Recent clinical studies have shown that HIV disease pathogenesis can depend
strongly on many factors at the time of transmission, including the strength of
the initial viral load and the local availability of CD4+ T-cells. In this
article, a new within-host model of HIV infection that incorporates the
homeostatic proliferation of T-cells is formulated and analyzed. Due to the
effects of this biological process, the influence of initial conditions on the
proliferation of HIV infection is further elucidated. The identifiability of
parameters within the model is investigated and a local stability analysis,
which displays additional complexity in comparison to previous models, is
conducted. The current study extends previous theoretical and computational
work on the early stages of the disease and leads to interesting nonlinear
dynamics, including a parameter region featuring bistability of infectious and
viral clearance equilibria and the appearance of a Hopf bifurcation within
biologically relevant parameter regimes.
An Adaptive Neuro-fuzzy Strategy in Closed-loop Control of Anesthesia
Date: 2019-10-14 00:00:00

This paper proposes an adaptive neuro-fuzzy framework to improve drug
infusion rate in closed-loop control of anesthesia. The proposed controller
provides a sub-optimal propofol administration rate as input to reach the
desired bispectral index, which is the output of the system, in both induction
and maintenance phases. In this controller, a critic agent assesses the plant
output and produces a reinforcement signal to adapt the controller parameters
and minimize the propofol administration rate. The controller is applied to a
conventional pharmacokinetic-pharmacodynamics model of anesthesia to evaluate
its applicability in closed loop-control of anesthesia. To simulate the
designed controller, physiological parameters of 12 patients are used in the
mathematical model. The simulation results show that the proposed controller
can overcome current challenges in the closed-loop control of anesthesia like
inter patient variability, model uncertainties and surgical disturbances
without overdose or underdose in a time range of 2 to 4 minutes. Analytical
comparison of results shows the strength of the controller in closed-loop
control of anesthesia.
Optimal Dynamic Futures Portfolio in a Regime-Switching Market Framework
Authors: Tim Leung, Yang Zhou
Date: 2019-10-14 00:00:00

We study the problem of dynamically trading futures in a regime-switching market. Modeling the underlying asset price as a Markov-modulated diffusion process, we present a utility maximization approach to determine the optimal futures trading strategy. This leads to the analysis of the associated system of Hamilton-Jacobi-Bellman (HJB) equations, which are reduced to a system of linear ODEs. We apply our stochastic framework to two models, namely, the Regime-Switching Geometric Brownian Motion (RS-GBM) model and Regime-Switching Exponential Ornstein-Uhlenbeck (RS-XOU) model. Numerical examples are provided to illustrate the investor's optimal futures positions and portfolio value across market regimes.
Precisamos de uma Contabilidade Ambiental para as "Amaz\^onias" Paraense?
Date: 2019-10-15 00:00:00

This paper has the following objectives: to understand the concepts of
Environmental Accounting in Brazil; Make criticisms and propositions anchored
in the reality or demand of environmental accounting for Amazonia Paraense. The
methodological strategy was a critical analysis of Ferreira's books (2007);
Ribeiro (2010) and Tinoco and Kraemer (2011) using their correlation with the
scientific production of authors discussing the Paraense Amazon, besides our
experience as researchers of this territory. As a result, we created three
sections: one for understanding the current constructs of environmental
accounting, one for criticism and one for propositions.
Grant-free Non-orthogonal Multiple Access for IoT: A Survey
Date: 2019-10-15 00:00:00
Tasks: Scheduling, Survey

Massive machine-type communications (mMTC) is one of the main three focus
areas in the 5th generation (5G) of mobile standards to enable connectivity of
a massive number of internet of things (IoT) devices with little or no human
intervention. In conventional human-type communications (HTC), due to the
limited number of available radio resources and orthogonal/non-overlapping
nature of existing resource allocation techniques, users need to compete for
connectivity through a random access (RA) process, which may turn into a
performance bottleneck in mMTC. In this context, non-orthogonal multiple access
(NOMA) has emerged as a potential technology that allows overlapping of
multiple users over a radio resource, thereby creating an opportunity to enable
more autonomous and grant-free communication, where devices can transmit data
whenever they need. The existing literature on NOMA schemes majorly considers
centralized scheduling based HTC, where users are already connected, and
various system parameters like spreading sequences, interleaving patterns,
power control, etc., are predefined. Contrary to HTC, mMTC traffic is different
with mostly uplink communication, small data size per device, diverse quality
of service, autonomous nature, and massive number of devices. Hence, the
signaling overhead and latency of centralized scheduling becomes a potential
performance bottleneck. To tackle this, grant-free access is needed, where mMTC
devices can autonomously transmit their data over randomly chosen radio
resources. This article, in contrast to existing surveys, comprehensively
discusses the recent advances in NOMA from a grant-free connectivity
perspective. Moreover, related practical challenges and future directions are
discussed.
To Recurse or not to Recurse,a Low Dose CT Study
Date: 2019-10-15 00:00:00
Tasks: Image Restoration

Restoring high-quality CT images from low dose CT counterparts is an
ill-posed, nonlinear problem to which Deep Learning approaches have been giving
superior solutions compared to classical model-based approaches. In this
article, a framework is presented wherein a Recurrent Neural Network (RNN) is
utilized to remove the streaking artefacts from low projection number CT
imaging. The results indicate similar image restoration performance for the RNN
compared to the feedforward network in low noise cases while in high noise
levels the RNN returns better results. The computational costs are also
compared between RNN and feedforward networks.
Identification of Behavioural Models for Railway Turnouts Monitoring
Date: 2019-10-15 00:00:00

This study introduces a low-complexity behavioural model to describe the
dynamic response of railway turnouts due to the ballast and railpad components.
The behavioural model should serve as the basis for the future development of a
supervisory system for the continuous monitoring of turnouts. A fourth order
linear model is proposed based on spectral analysis of measured rail vertical
accelerations gathered during a receptance test and it is then identified at
several sections of the turnout applying the Eigensystem Realization Algorithm.
The predictviness and robustness of the behavioural models have been assessed
on a large data set of train passages differing for train type, speed and
loading condition. Last, the need for a novel modeling method is argued in
relation to high-fidelity mechanistic models widely used in the railway
engineering community.
Wideband Bandpass Filters Using a Novel Thick Metallization Technology
Date: 2019-10-15 00:00:00

A new class of wideband bandpass filters based on using thick metallic bars
as microwave resonators, instead of common microstrip lines, is presented.
These bars provide a series of advantages over fully planar printed
technologies, including higher coupling levels between resonators, better
unloaded quality factors QU, and larger bandwidths, implemented with more
compact structures. Moreover, thick bar resonators can easily be coupled to an
additional resonance excited in a box used for shielding, allowing to realize
transversal topologies able to implement transmission zeros at desired
frequencies. To illustrate the capabilities of this technology, two microwave
filters with different bandwidths and one transmission zero have been designed.
One of the filters has been manufactured and tested using copper bars inside an
aluminum housing partially filled with Teflon. Measured data demonstrates a
fractional bandwidth about FBW = 32%, spurious free range SFR > 50%, unloaded
quality factor of QU = 1180 and return losses over 20 dB without requiring any
post-tuning on the prototype, confirming the exciting performance of the
proposed technology.