pwc-archive/papers-with-abstracts huggingface dataset:
Why Long-Term Debt Instruments Cannot Be Deposit Substitutes
Date: 2017-12-28 00:00:00

The definition of deposit substitutes in Philippine tax law fails to consider
the maturity of a debt instrument. This makes it possible for long-term bonds
to be considered as deposit substitutes if they meet the 20-lender rule,
taxable at 20% final tax. However, long-term debt instruments cannot
realistically function as deposit substitutes even if they fall in the hands of
20 or more lenders. First, long-term debt instruments cannot simultaneously
replicate the twin features of capital preservation and liquidity, which are
integral to the nature of a deposit substitute. Second, deposit substitutes are
an integral part of the maturity transformation process (i.e. short-term
borrowing for the purpose of long-term lending) in financial intermediaries,
which means that they should have low borrowing cost, made possible only by
having short-term maturity. To prove these propositions, this paper situates
the function of deposit substitutes within the context of shadow banking, where
said instruments originated and are generally used. To show the incompatibility
between a deposit substitute and a long-term debt instrument, the paper applies
the fundamental theory of bond values to 10-year zero-coupon treasury notes
called 'PEACe Bonds' in Banco De Oro, et al. vs. Republic (2015 and 2016). The
paper recommends that deposit substitutes should be limited to debt instruments
with maturity of not more than 1 year.
How Short Sales Circumvent the Capital Gains Tax System
Date: 2017-12-28 00:00:00

Through a short sale, a person borrows a share of stock from a lender, sells
the borrowed share to a third person at the current price, and purchases an
identical share in the market at a future date and at a future price to replace
the borrowed share of stock. This only makes sense if the short seller
anticipates a downward trend in share price. The short seller incurs a gain if
share price decreases because the cost of replacing the borrowed share falls
below the selling price. The reverse is true in an ordinary sale, where a
person owning a share of stock incurs a loss if price decreases because the
selling price falls below the basis or acquisition cost. Therefore, when a
taxpayer simultaneously owns a share of stock and short sells an identical
stock, any gain in an ordinary sale of the owned stock is offset by a
corresponding loss in the short sale of the borrowed identical stock, vice
versa. This offsetting effect, in turn, creates an unexpected tax deferral
opportunity abused in other jurisdictions and which remains unregulated in the
Philippine tax system.
OPARC: Optimal and Precise Array Response Control Algorithm -- Part II: Multi-points and Applications
Date: 2017-12-29 00:00:00

In this paper, the optimal and precise array response control (OPARC)
algorithm proposed in Part I of this two paper series is extended from single
point to multi-points. Two computationally attractive parameter determination
approaches are provided to maximize the array gain under certain constraints.
In addition, the applications of the multi-point OPARC algorithm to array
signal processing are studied. It is applied to realize array pattern synthesis
(including the general array case and the large array case), multi-constraint
adaptive beamforming and quiescent pattern control, where an innovative concept
of normalized covariance matrix loading (NCL) is proposed. Finally, simulation
results are presented to validate the superiority and effectiveness of the
multi-point OPARC algorithm.
Variance swaps under L\'{e}vy process with stochastic volatility and stochastic interest rate in incomplete markets
Date: 2018-03-14 00:00:00

This paper focuses on the pricing of the variance swap in an incomplete
market where the stochastic interest rate and the price of the stock are
respectively driven by Cox-Ingersoll-Ross model and Heston model with
simultaneous L\'{e}vy jumps. By using the equilibrium framework, we obtain the
pricing kernel and the equivalent martingale measure. Moreover, under the
forward measure instead of the risk neural measure, we give the closed-form
solution for the fair delivery price of the discretely sampled variance swap by
employing the joint moment generating function of the underlying processes.
Finally, we provide some numerical examples to depict that the values of
variance swaps not only depend on the stochastic interest rates but also
increase in the presence of jump risks.
Meeting yield and conservation objectives by balancing harvesting of juveniles and adults
Date: 2019-02-25 00:00:00

Sustainable yields that are at least 80% of the maximum sustainable yield are
sometimes referred to as pretty good yield (PGY). The range of PGY harvesting
strategies is generally broad and thus leaves room to account for additional
objectives besides high yield. Here, we analyze stage-dependent harvesting
strategies that realize PGY with conservation as a second objective. We show
that (1) PGY harvesting strategies can give large conservation benefits and (2)
equal harvesting rates of juveniles and adults is often a good strategy. These
conclusions are based on trade-off curves between yield and four measures of
conservation that form in two established population models, one age-structured
and one stage-structured model, when considering different harvesting rates of
juveniles and adults. These conclusions hold for a broad range of parameter
settings, though our investigation of robustness also reveals that (3)
predictions of the age-structured model are more sensitive to variations in
parameter values than those of the stage-structured model. Finally, we find
that (4) measures of stability that are often quite difficult to assess in the
field (e.g.~basic reproduction ratio and resilience) are systematically
negatively correlated with impacts on biomass and impact on size structure, so
that these later quantities can provide integrative signals to detect possible
collapses.
Spectral analysis for nonstationary audio
Date: 2018-08-23 00:00:00

A new approach for the analysis of nonstationary signals is proposed, with a
focus on audio applications. Following earlier contributions, nonstationarity
is modeled via stationarity-breaking operators acting on Gaussian stationary
random signals. The focus is on time warping and amplitude modulation, and an
approximate maximum-likelihood approach based on suitable approximations in the
wavelet transform domain is developed. This paper provides theoretical analysis
of the approximations, and introduces JEFAS, a corresponding estimation
algorithm. The latter is tested and validated on synthetic as well as real
audio signal.
Foreign Portfolio Investment and Economy: The Network Perspective
Date: 2017-12-29 00:00:00

The European Union and Eurozone present an inquisitive case of strongly
interconnected network with high degree of dependence among nodes. This
research focused on investment network of European Union and its major trading
partners for specific time period 2001 to 2014. The changing investment
patterns within Eurozone suggest strong financial and trade links with central
and large economies. This study is about the association between portfolio
investment and economic indicators with respect to financial networks. The
analysis used the strongly connected investment network of Eurozone and its
large trading partners. A strong correlation between, increasing or decreasing
investment patterns with economic indicators of particular economy was found.
Interestingly correlation patterns for network members other than Eurozone
states were not as strong and depicted mild behavior. This as well, explains
the significance of interconnectedness level among nodes of one network with
varying centrality measures. Investment network visualization techniques helped
to validate the results based on network`s statistical measures.
Communications and Control for Wireless Drone-Based Antenna Array
Date: 2018-09-25 00:00:00

In this paper, the effective use of multiple quadrotor drones as an aerial
antenna array that provides wireless service to ground users is investigated.
In particular, under the goal of minimizing the airborne service time needed
for communicating with ground users, a novel framework for deploying and
operating a drone-based antenna array system whose elements are single-antenna
drones is proposed. In the considered model, the service time is minimized by
minimizing the wireless transmission time as well as the control time that is
needed for movement and stabilization of the drones. To minimize the
transmission time, first, the antenna array gain is maximized by optimizing the
drone spacing within the array. In this case, using perturbation techniques,
the drone spacing optimization problem is addressed by solving successive,
perturbed convex optimization problems. Then, the optimal locations of the
drones around the array's center are derived such that the transmission time
for the user is minimized. Given the determined optimal locations of drones,
the drones must spend a control time to adjust their positions dynamically so
as to serve multiple users. To minimize this control time of the quadrotor
drones, the speed of rotors is optimally adjusted based on both the
destinations of the drones and external forces (e.g., wind and gravity). In
particular, using bang-bang control theory, the optimal rotors' speeds as well
as the minimum control time are derived in closed-form. Simulation results show
that the proposed approach can significantly reduce the service time to ground
users compared to a fixed-array case in which the same number of drones form a
fixed uniform antenna array. The results also show that, in comparison with the
fixed-array case, the network's spectral efficiency can be improved by 32%
while leveraging the drone antenna array system.
9.5 Hypotheses on the Informational Structure of Life which are Multi-scale, Human-readable, Provocative, Coherent, Plausible, Falsifiable, and Actionable
Date: 2017-12-28 00:00:00
Tasks: Nutrition

This document has an unorthodox structure to accomplish an unorthodox goal:
presenting the scaffolding for a zero-parameter Unified Theory of Life inside
several dozen pages. Ideally, that length is short enough to be understood in a
few hours, yet complete and principled enough to unfold into solutions to
civilization's most urgent problem: the accelerating de-calibration of fluid
human brains by compelling digitized signals. The stakes of understanding
humans as informational beings are huge. Without drastic changes, crucial human
functionality will vanish in a generation. Fortunately, the cures are cheap and
easy if promoted properly. These ten hypotheses re-present in more abstract,
encapsulated form a Framework published a few months ago in a reputable
peer-reviewed journal, which concluded that human sensory systems must be
accorded data of the same quality as the data which already trains algorithmic
intelligences. While the lack of experiment-grade detail might make these
hypotheses seem "unscientific," in compensation the breadth of this Framework
ought to provide the virtues of theory: clarity, simplicity, coherence, and
self-evidence. Five of the hypotheses span humanity's current problem-space,
and five a possible solution space. They are: H1.0 Stably evolving
distributions must balance themselves between narrowing and broadening H2.0
Stabilization and homeostasis are fragile in multiple ways H3.0 Representing
spacetime requires micro-timing and mega-assumptions H4.0 When in doubt, ping!
H5.0 Mediated communication becomes infected with pinging H6.0 If scaling and
incentives are the problems, then entropy and affection are the solutions H7.0
"Paleo everything but violence" provides sensorimotor nutrition H8.0 Humans
evolved to resonate ecstatically H9.0 Symmetric spinal health syndrome H9.5
Helping the irresistible force beat the immovable object
High Speed All-optical extended DV-Curve-based DNA sequence alignment utilizing wavelength and polarization modulation
Date: 2017-12-27 00:00:00
Tasks: All

This paper presents a novel optical processing approach for exploring genome
sequences built upon optical correlator for global alignment and extended
DV-curve method for local alignment. To overcome the problem of traditional
DV-curve method for presenting an accurate and simplified output, we propose
HAWPOD, built upon DV- curve method, to analyze genome sequences in five steps:
DNA coding, alignment, noise cancellation, simplification, and modification.
Moreover, all-optical implementation of the HAWPOD method is developed, while
its accuracy is validated through numerical simulations in LUMERICAL FDTD. The
results express the proposed method is much faster than its electrical
counterparts, such as Basic Local Alignment Search Tools.
An Unsupervised Homogenization Pipeline for Clustering Similar Patients using Electronic Health Record Data
Date: 2018-03-21 00:00:00
Tasks: Clustering, Diagnostic, Imputation

Electronic health records (EHR) contain a large variety of information on the
clinical history of patients such as vital signs, demographics, diagnostic
codes and imaging data. The enormous potential for discovery in this rich
dataset is hampered by its complexity and heterogeneity.
  We present the first study to assess unsupervised homogenization pipelines
designed for EHR clustering. To identify the optimal pipeline, we tested
accuracy on simulated data with varying amounts of redundancy, heterogeneity,
and missingness. We identified two optimal pipelines: 1) Multiple Imputation by
Chained Equations (MICE) combined with Local Linear Embedding; and 2) MICE,
Z-scoring, and Deep Autoencoders.
Logarithmic Frequency Scaling and Consistent Frequency Coverage for the Selection of Auditory Filterbank Center Frequencies
Date: 2017-12-30 00:00:00

This paper provides new insights into the problem of selecting filter center
frequencies for the auditory filterbanks. We propose to use a constant
frequency distance and a consistent frequency coverage as the two metrics that
motivate the logarithmic frequency scaling and a regularized selection of
center frequencies. The frequency scaling and the consistent frequency coverage
have been derived based on a common harmonic speaker signal model. Furthermore,
we have found that the existing linear equivalent rectangular bandwidth (ERB)
function as well as any possible linear ERB approximation can also lead to a
consistent frequency coverage. The results are verified and demonstrated using
the gammatone filterbank.
PrivySense: $\underline{Pri}$ce $\underline{V}$olatilit$\underline{y}$ based $\underline{Sen}$timent$\underline{s}$ $\underline{E}$stimation from Financial News using Machine Learning
Date: 2018-02-21 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

As machine learning ascends the peak of computer science zeitgeist, the usage
and experimentation with sentiment analysis using various forms of textual data
seems pervasive. The effect is especially pronounced in formulating securities
trading strategies, due to a plethora of reasons including the relative ease of
implementation and the abundance of academic research suggesting automated
sentiment analysis can be productively used in trading strategies. The source
data for such analyzers ranges a broad spectrum like social media feeds,
micro-blogs, real-time news feeds, ex-post financial data etc. The abstract
technique underlying these analyzers involve supervised learning of sentiment
classification where the classifier is trained on annotated source corpus, and
accuracy is measured by testing how well the classifiers generalizes on unseen
test data from the corpus. Post training, and validation of fitted models, the
classifiers are used to execute trading strategies, and the corresponding
returns are compared with appropriate benchmark returns (for e.g., the S&P500
returns).
  In this paper, we introduce $\underline{a\ novel\ technique\ of\ using\
price\ volatilities\ to\ empirically\ determine\ the\ sentiment\ in\ news\
data}$, instead of the traditional reverse approach. We also perform meta
sentiment analysis by evaluating the efficacy of existing sentiment classifiers
and the precise definition of sentiment from securities trading context. We
scrutinize the efficacy of using human-annotated sentiment classification and
the tacit assumptions that introduces subjective bias in existing financial
news sentiment classifiers.
Image Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations
Date: 2017-12-30 00:00:00
Tasks: Image Inpainting

Image inpainting is a restoration process which has numerous applications.
Restoring of scanned old images with scratches, or removing objects in images
are some of inpainting applications. Different approaches have been used for
implementation of inpainting algorithms. Interpolation approaches only consider
one direction for this purpose. In this paper we present a new perspective to
image inpainting. We consider multiple directions and apply both
one-dimensional and two-dimensional bicubic interpolations. Neighboring pixels
are selected in a hyperbolic formation to better preserve corner pixels. We
compare our work with recent inpainting approaches to show our superior
results.
Does the Cross-Talk Between Nonlinear Modes Limit the Performance of NFDM Systems?
Date: 2017-12-30 00:00:00

We show a non-negligible cross-talk between nonlinear modes in Nonlinear
Frequency-Division Multiplexed system when data is modulated over the nonlinear
Fourier spectrum, both the continuous spectrum and the discrete spectrum, and
transmitted over a lumped amplified fiber link. We evaluate the performance
loss if the cross-talks are neglected.
Non-invasive biomarkers of fetal brain development reflecting prenatal stress: an integrative multi-scale multi-species perspective on data collection and analysis
Date: 2017-12-31 00:00:00

Prenatal stress (PS) impacts early postnatal behavioural and cognitive
development. This process of 'fetal programming' is mediated by the effects of
the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA)
axis and autonomic nervous system (ANS). The HPA axis is a dynamic system
regulating homeostasis, especially the stress response, and is highly sensitive
to adverse early life experiences. We review the evidence for the effects of PS
on fetal programming of the HPA axis and the ANS. We derive a multi-scale
multi-species approach to devising preclinical and clinical studies to identify
early non-invasively available pre- and postnatal biomarkers of these
programming effects. Such approach would identify adverse postnatal brain
developmental trajectories, a prerequisite for designing therapeutic
interventions. The multiple scales include the biomarkers reflecting changes in
the brain epigenome, metabolome, microbiome and the ANS activity gauged via an
array of advanced non-invasively obtainable properties of fetal heart rate
fluctuations. The proposed framework has the potential to reveal mechanistic
links between maternal stress during pregnancy and changes across these
physiological scales. Such biomarkers may hence be useful as early and
non-invasive predictors of neurodevelopmental trajectories influenced by the
PS. We conclude that studies into PS effects must be conducted on multiple
scales derived from concerted observations in multiple animal models and human
cohorts performed in an interactive and iterative manner and deploying machine
learning for data synthesis, identification and validation of the best
non-invasive biomarkers.
Confidence set for group membership
Authors: Andreas Dzemski, Ryo Okui
Date: 2017-12-31 00:00:00

Our confidence set quantifies the statistical uncertainty from data-driven group assignments in grouped panel models. It covers the true group memberships jointly for all units with pre-specified probability and is constructed by inverting many simultaneous unit-specific one-sided tests for group membership. We justify our approach under $N, T \to \infty$ asymptotics using tools from high-dimensional statistics, some of which we extend in this paper. We provide Monte Carlo evidence that the confidence set has adequate coverage in finite samples.An empirical application illustrates the use of our confidence set.
Transition probability of Brownian motion in the octant and its application to default modeling
Date: 2018-05-23 00:00:00

We derive a semi-analytic formula for the transition probability of
three-dimensional Brownian motion in the positive octant with absorption at the
boundaries. Separation of variables in spherical coordinates leads to an
eigenvalue problem for the resulting boundary value problem in the two angular
components. The main theoretical result is a solution to the original problem
expressed as an expansion into special functions and an eigenvalue which has to
be chosen to allow a matching of the boundary condition. We discuss and test
several computational methods to solve a finite-dimensional approximation to
this nonlinear eigenvalue problem. Finally, we apply our results to the
computation of default probabilities and credit valuation adjustments in a
structural credit model with mutual liabilities.
Resource Abundance and Life Expectancy
Date: 2017-12-31 00:00:00

This paper investigates the impacts of major natural resource discoveries
since 1960 on life expectancy in the nations that they were resource poor prior
to the discoveries. Previous literature explains the relation between nations
wealth and life expectancy, but it has been silent about the impacts of
resource discoveries on life expectancy. We attempt to fill this gap in this
study. An important advantage of this study is that as the previous researchers
argued resource discovery could be an exogenous variable. We use longitudinal
data from 1960 to 2014 and we apply three modern empirical methods including
Difference-in-Differences, Event studies, and Synthetic Control approach, to
investigate the main question of the research which is 'how resource
discoveries affect life expectancy?'. The findings show that resource
discoveries in Ecuador, Yemen, Oman, and Equatorial Guinea have positive and
significant impacts on life expectancy, but the effects for the European
countries are mostly negative.
Autoregressive Point-Processes as Latent State-Space Models: a Moment-Closure Approach to Fluctuations and Autocorrelations
Date: 2018-02-28 00:00:00
Tasks: Point Processes, State Space Models

Modeling and interpreting spike train data is a task of central importance in
computational neuroscience, with significant translational implications. Two
popular classes of data-driven models for this task are autoregressive Point
Process Generalized Linear models (PPGLM) and latent State-Space models (SSM)
with point-process observations. In this letter, we derive a mathematical
connection between these two classes of models. By introducing an auxiliary
history process, we represent exactly a PPGLM in terms of a latent, infinite
dimensional dynamical system, which can then be mapped onto an SSM by basis
function projections and moment closure. This representation provides a new
perspective on widely used methods for modeling spike data, and also suggests
novel algorithmic approaches to fitting such models. We illustrate our results
on a phasic bursting neuron model, showing that our proposed approach provides
an accurate and efficient way to capture neural dynamics.