pwc-archive/papers-with-abstracts huggingface dataset:
The Theoretical Price of a Share-Based Payment with Performance Conditions and Implications for the Current Accounting Standards
Date: 2018-06-14 00:00:00

Although the growth of share-based payments with performance conditions
(hereafter, SPPC) is prominent today, the theoretical price of SPPC has not
been sufficiently studied. Reflecting such a situation, the current accounting
standards for share-based payments issued in 2004 have had many problems. This
paper develops a theoretical SPPC price model with a framework for a marginal
utility-based price, which previous studies proposed is the price of contingent
claims in an incomplete market. This paper's contribution is fivefold. First,
we restricted the stochastic process to a certain class to demonstrate how to
consistently change all variables' probability distributions, which affect the
SPPC payoff. Second, we explicitly indicated not only the stochastic processes
of the stock price process and performance variables under the changed
probability, but also how the changes in the performance variables' drift
coefficients related to stock betas. Third, we proposed a convenient model in
application that uses only a few parameters. Fourth, we provided a method to
estimate the parameters and improve the estimation of both the price and
parameters. Fifth, we illustrated the problems in current accounting standards
and indicated how the theoretical price model can significantly improve them.
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing
Date: 2018-06-15 00:00:00
Tasks: compressed sensing, Quantization

We present a novel scheme allowing for 2D target localization using highly
quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW)
radar with two receiving antennas. Quantization of radar signals introduces
localization artifacts, we remove this limitation by inserting a dithering on
the unquantized observations. We then adapt the projected back projection
algorithm to estimate both the range and angle of targets from the dithered
quantized radar observations, with provably decaying reconstruction error when
the number of observations increases. Simulations are performed to highlight
the accuracy of the dithered scheme in noiseless conditions when compared to
the non-dithered and full 32-bit resolution under severe bit-rate reduction.
Finally, measurements are performed using a radar sensor to demonstrate the
effectiveness and performances of the proposed quantized dithered scheme in
real conditions.
A metapopulation model of the spread of the Devil Facial Tumour Disease predicts the long term collapse of its host but not its extinction
Date: 2018-06-14 00:00:00

The Devil Facial Tumour Disease (DFTD), a unique case of a transmissible
cancer, had a devastating effect on its host, the Tasmanian Devil. Current
estimates of its density are at roughly 20% of the pre-disease state, and
single-population epidemiological models have predicted the likely extinction
of the host. Here we take advantage of extensive surveys across Tasmania
providing data on the spatial and temporal spread of DFTD, and investigate the
dynamics of this host-pathogen system using a spatial metapopulation model. We
first confirm a most likely origin of DFTD in the north-east corner of the
island, and then use the inferred dynamics to predict the fate of the species.
We find that our medium-term predictions match additional data not used for
fitting, and that on the long-term, Tasmanian Devils are predicted to coexist
with the tumour. The key process allowing persistence is the repeated
reinvasion of extinct patches from neighbouring areas where the disease has
flared up and died out, resulting in a dynamic equilibrium with different
levels of spatial heterogeneity. However, this dynamic equilibrium is predicted
to keep this apex predator at about 9 % of its original density, with possible
dramatic effects on the Tasmanian ecosystem.
Are My EHRs Private Enough? -Event-level Privacy Protection
Authors: Mao Chengsheng, Zhao Yuan, Sun Mengxin, Luo Yuan
Date: 2018-06-12 00:00:00

Privacy is a major concern in sharing human subject data to researchers for
secondary analyses. A simple binary consent (opt-in or not) may significantly
reduce the amount of sharable data, since many patients might only be concerned
about a few sensitive medical conditions rather than the entire medical
records. We propose event-level privacy protection, and develop a feature
ablation method to protect event-level privacy in electronic medical records.
Using a list of 13 sensitive diagnoses, we evaluate the feasibility and the
efficacy of the proposed method. As feature ablation progresses, the
identifiability of a sensitive medical condition decreases with varying speeds
on different diseases. We find that these sensitive diagnoses can be divided
into 3 categories: (1) 5 diseases have fast declining identifiability (AUC
below 0.6 with less than 400 features excluded); (2) 7 diseases with
progressively declining identifiability (AUC below 0.7 with between 200 and 700
features excluded); and (3) 1 disease with slowly declining identifiability
(AUC above 0.7 with 1000 features excluded). The fact that the majority (12 out
of 13) of the sensitive diseases fall into the first two categories suggests
the potential of the proposed feature ablation method as a solution for
event-level record privacy protection.
Analyzing counterintuitive data
Authors: E. Doty, N. McCague, D. J. Stone, L. A. Celi
Date: 2018-06-12 00:00:00

Purpose: To explore the issue of counterintuitive data via analysis of a representative case and further discussion of those situations in which the data appear to be inconsistent with current knowledge. Case: 844 postoperative CABG patients, who were extubated within 24 hours of surgery were identified in a critical care database (MIMIC-III). Nurse elicited pain scores were documented throughout their hospital stay on a scale of 0 to 10. Levels were tracked as mean, median, and maximum values, and categorized as no (0/10), mild (1-3), moderate (4-6) and severe pain (7-10). Regression analysis was employed to analyze the relationship between pain scores and outcomes of interest (mortality and hospital LOS). After covariate adjustment, increased levels of pain were found to be associated with lower mortality rates and reduced hospital LOS. Conclusion: These counterintuitive results for post-CABG pain related outcomes have not been previously reported. While not representing strong enough evidence to alter clinical practice, confirmed and reliable results such as these should serve as a research trigger and prompt further studies into unexpected associations between pain and patient outcomes. With the advent of frequent secondary analysis of electronic health records, such counterintuitive data results are likely to become more frequent. We discuss the issue of counterintuitive data in extended fashion, including possible reasons for, and approaches to, this phenomenon.
Martingales and Super-martingales Relative to a Convex Set of Equivalent Measures
Date: 2018-06-13 00:00:00

In the paper, the martingales and super-martingales relative to a convex set
of equivalent measures are systematically studied. The notion of local regular
super-martingale relative to a convex set of equivalent measures is introduced
and the necessary and sufficient conditions of the local regularity of it in
the discrete case are founded. The description of all local regular
super-martingales relative to a convex set of equivalent measures is presented.
The notion of the complete set of equivalent measures is introduced. We prove
that every bounded in some sense super-martingale relative to the complete set
of equivalent measures is local regular. A new definition of the fair price of
contingent claim in an incomplete market is given and the formula for the fair
price of Standard Option of European type is found. The proved Theorems are the
generalization of the famous Doob decomposition for super-martingale onto the
case of super-martingales relative to a convex set of equivalent measures.
A Generalized Framework for Simultaneous Long-Short Feedback Trading
Authors: Joseph D. O'Brien, Mark E. Burke, Kevin Burke
Date: 2018-06-14 00:00:00

We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer to this new strategy as Generalized SLS (GSLS). Furthermore, we investigate the conditions under which positive gain can be assured within the GSLS setup for both deterministic stock price evolution and geometric Brownian motion. In contrast to existing literature in this area (which places little emphasis on the practical application of SLS strategies), we suggest optimization procedures for selecting the control parameters based on historical data, and we extensively test these procedures across a large number of real stock price trajectories (495 in total). We find that the implementation of such optimization procedures greatly improves the performance compared with fixing control parameters, and, indeed, the GSLS strategy outperforms the simpler SLS strategy in general.
A new approach for American option pricing: The Dynamic Chebyshev method
Date: 2018-06-14 00:00:00

We introduce a new method to price American options based on Chebyshev
interpolation. In each step of a dynamic programming time-stepping we
approximate the value function with Chebyshev polynomials. The key advantage of
this approach is that it allows to shift the model-dependent computations into
an offline phase prior to the time-stepping. In the offline part a family of
generalised (conditional) moments is computed by an appropriate numerical
technique such as a Monte Carlo, PDE or Fourier transform based method. Thanks
to this methodological flexibility the approach applies to a large variety of
models. Online, the backward induction is solved on a discrete Chebyshev grid,
and no (conditional) expectations need to be computed. For each time step the
method delivers a closed form approximation of the price function along with
the options' delta and gamma. Moreover, the same family of (conditional)
moments yield multiple outputs including the option prices for different
strikes, maturities and different payoff profiles. We provide a theoretical
error analysis and find conditions that imply explicit error bounds for a
variety of stock price models. Numerical experiments confirm the fast
convergence of prices and sensitivities. An empirical investigation of accuracy
and runtime also shows an efficiency gain compared with the least-square
Monte-Carlo method introduced by Longstaff and Schwartz (2001).
A Convex Hull Based Approach for MIMO Radar Waveform Design with Quantized Phases
Date: 2018-06-14 00:00:00
Tasks: Radar waveform design

In this letter, we focus on designing constant-modulus waveform with discrete
phases for the multi-input multi-output (MIMO) radar, where the
signal-to-interference-plus-noise ratio (SINR) is maximized in the presence of
both the signal-dependent clutter and the noise. Given the NP-hardness of the
formulated problem, we propose to relax the original optimization as a sequence
of continuous quadratic programming (QP) subproblems by use of the convex hull
of the discrete feasible region, which yields approximated solutions with much
lower computational costs. Finally, we assess the effectiveness of the proposed
waveform design approach by numerical simulations.
Applications of Various Space-time Transformations to Determine Radar Signal Distortion Caused by a Moving Target Having Constant Velocity and Acceleration
Date: 2018-06-14 00:00:00

The effects of target motion on the distortion of radar signals are
investigated using five transformations, namely, Hsu, Lorentz, Galilean,
Reference, and Classical transformation equations. Hsu transformation is used
as a primary transformation since it expresses the temporal and spatial
transformations between an inertial reference frame and accelerating frame
where the origin of the accelerating frame has an initial velocity and
acceleration with respect to the inertial frame. Additionally, as the
acceleration approach zero and infinity, respectively, the Lorentz and Galilean
transformations are obtained. These transformations are used to express the
transmitted waveforms in the radar reference frame variables to that of the
target reference frame variables, and the reflected waveform from the target in
its reference frame variable to that of the radar reference frame variables,
leading to the distorted waveform received at the radar receiver due to the
motion of the target.
Personalized Context-Aware Point of Interest Recommendation
Authors: Aliannejadi Mohammad, Crestani Fabio
Date: 2018-06-14 00:00:00
Tasks: Learning-To-Rank

Personalized recommendation of Points of Interest (POIs) plays a key role in
satisfying users on Location-Based Social Networks (LBSNs). In this paper, we
propose a probabilistic model to find the mapping between user-annotated tags
and locations' taste keywords. Furthermore, we introduce a dataset on
locations' contextual appropriateness and demonstrate its usefulness in
predicting the contextual relevance of locations. We investigate four
approaches to use our proposed mapping for addressing the data sparsity
problem: one model to reduce the dimensionality of location taste keywords and
three models to predict user tags for a new location. Moreover, we present
different scores calculated from multiple LBSNs and show how we incorporate new
information from the mapping into a POI recommendation approach. Then, the
computed scores are integrated using learning to rank techniques. The
experiments on two TREC datasets show the effectiveness of our approach,
beating state-of-the-art methods.
How a well-adapting immune system remembers
Date: 2018-11-13 00:00:00

An adaptive agent predicting the future state of an environment must weigh
trust in new observations against prior experiences. In this light, we propose
a view of the adaptive immune system as a dynamic Bayesian machinery that
updates its memory repertoire by balancing evidence from new pathogen
encounters against past experience of infection to predict and prepare for
future threats. This framework links the observed initial rapid increase of the
memory pool early in life followed by a mid-life plateau to the ease of
learning salient features of sparse environments. We also derive a modulated
memory pool update rule in agreement with current vaccine response experiments.
Our results suggest that pathogenic environments are sparse and that memory
repertoires significantly decrease infection costs even with moderate sampling.
The predicted optimal update scheme maps onto commonly considered competitive
dynamics for antigen receptors.
Accurate and Robust Localization Techniques for Wireless Sensor Networks
Date: 2018-06-14 00:00:00

The report focuses on three areas in particular: the first is the Received
Signal Strength indicator technique, Direction of Arrival technique, and the
integration of two algorithms, RSS and DOA, in order to build a hybrid, more
robust algorithms.
  In the Received Signal Strength (RSS), the unknown node location is estimated
using trilateration. This report examines the performance of different
estimators such as Least Square, Weighted Least Square, and Huber robustness in
order to obtain the most robust performance.
  In the direction of arrival (DOA) method, the estimation is carried out using
Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimation of Signal
Parameters Via Rotational Invariance Technique (ESPRIT) algorithms. We
investigate multiple signal scenarios utilizing various antenna geometries,
which includes uniform linear array (ULA) and uniform circular array (UCA).
Specific attention is given for multipath scenarios in which signals become
spatially correlated (or coherent). This required the use of pre-processing
techniques, which include phase mode excitation (PME), spatial smoothing (SS),
and Toeplitz.
  Further improvements of existing localization techniques are demonstrated
through the use of a hybrid approach in which various combinations of RSS and
DOA are explored, simulated, and analyzed. This has led to two major
contributions: the first contribution is a combined RSS/DOA method, based on
UCA, which has the tolerance of detecting both uncorrelated and coherent
signals simultaneously. The second major contribution is a combined
Root-MUSIC/Toepltiz method, based on UCA, which is outperforms other techniques
in terms of increased number of detected signals and reduced computationally
load.
A Comparative Study of Cohesive Zone Models for Predicting Delamination Behaviors of Arterial Wall
Date: 2018-06-15 00:00:00

Arterial tissue delamination, manifested as the failure between arterial
layers, is a critical process in the rupture of atherosclerotic plaque, leading
to potential life-threatening clinical consequences. Numerous models have been
used to characterize the arterial tissue delamination. Few has investigated the
effect of Cohesive Zone Model (CZM) shapes on predicting the delamination
behavior of the arterial wall. In this study, four types of cohesive zone
models (triangular, trapezoidal, linear-exponential and exponential-linear)
were investigated to compare their predictability of the arterial wall failure.
The Holzapfel-Gasser-Ogden (HGO) model was adopted for modelling the mechanical
behavior of the aortic bulk material. The simulation results using CZM on the
aortic media delamination were also compared with the results on mouse plaque
delamination and human fibrous cap delamination. The results show that: 1) the
simulation results based on the four shapes of CZMs match well with the
experimental results, 2) the triangular and exponential-linear CZMs are in good
agreement with the experimental force-displacement curves of mouse plaque
delamination, 3) considering the viscoelastic effect of the arterial tissue,
the triangular and exponential-linear CZMs match well with the experimental
force-displacement curves of human fibrous cap delamination. Thus, triangular
and exponential-linear CZMs can capture the arterial tissue failure response
well.
A mathematical model for chemoimmunotherapy of chronic lymphocytic leukemia
Date: 2018-06-19 00:00:00

Immunotherapy is currently regarded as the most promising treatment to fight
against cancer. This is particularly true in the treatment of chronic
lymphocytic leukemia, an indolent neoplastic disease of B-lymphocytes which
eventually causes the immune system's failure. In this and other areas of
cancer research, mathematical modeling is pointed out as a prominent tool to
analyze theoretical and practical issues. Its lack in studies of
chemoimmunotherapy of chronic lymphocytic leukemia is what motivates us to come
up with a simple ordinary differential equation model. It is based on ideas of
de Pillis & Radunskaya and on standard pharmacokinetics-pharmacodynamics
assumptions. In order to check the positivity of the state variables, we first
establish an invariant region where these time-dependent variables remain
positive. Afterwards, the action of the immune system, as well as the
chemoimmunotherapeutic role in promoting cancer cure are investigated by means
of numerical simulations and the classical linear stability analysis. The role
of adoptive cellular immunotherapy is also addressed. Our overall conclusion is
that chemoimmunotherapeutic protocols can be effective in treating chronic
lymphocytic leukemia provided that chemotherapy is not a limiting factor to the
immunotherapy efficacy.
Dispatch and Primary Frequency Control with Electrochemical Storage: a System-wise Verification
Date: 2018-06-15 00:00:00

Uncertainty levels in forecasting of renewable generation and demand are
known to affect the amount of reserve required to operate the power grid with a
given level of reliability. In this paper, we quantify the effects on the
system reserve and reliability, due to the local dispatch of stochastic demand
and renewable generation. The analysis is performed considering the model of
the IEEE 39-bus system, with detailed dynamic models of conventional
generation, wind generation, demand and an under-frequency load shedding
mechanism. The analysis compares to cases: the base case, where renewable
generation and demand power are stochastic and the power reserve is provided by
conventional generation, against the case where the operation of traditionally
stochastic resources is dispatched according to pre-established dispatch plans
thanks to controlling local flexibility. Simulations reproduce the
post-contingency dynamic behavior of the grid due to outages of generators. The
contingencies are selected to trigger under frequency load shedding mechanisms,
hence to demonstrate the different levels of system operation reliability for
the two case studies. Simulation results show that dispatching traditionally
stochastic generation scores better regarding to expected energy not served,
producing an increase of the system reliability.
Optimal Market Making in the Presence of Latency
Authors: Xuefeng Gao, Yunhan Wang
Date: 2018-06-15 00:00:00

This paper studies optimal market making for large-tick assets in the presence of latency. We consider a random walk model for the asset price, and formulate the market maker's optimization problem using Markov Decision Processes (MDP). We characterize the value of an order and show that it plays the role of one-period reward in the MDP model. Based on this characterization, we provide explicit criteria for assessing the profitability of market making when there is latency. Under our model, we show that a market maker can earn a positive expected profit if there are sufficient uninformed market orders hitting the market maker's limit orders compared with the rate of price jumps, and the trading horizon is sufficiently long. In addition, our theoretical and numerical results suggest that latency can be an additional source of risk and latency impacts negatively the performance of market makers.
Computation of option greeks under hybrid stochastic volatility models via Malliavin calculus
Date: 2018-06-11 00:00:00

This study introduces computation of option sensitivities (Greeks) using the
Malliavin calculus under the assumption that the underlying asset and interest
rate both evolve from a stochastic volatility model and a stochastic interest
rate model, respectively. Therefore, it integrates the recent developments in
the Malliavin calculus for the computation of Greeks: Delta, Vega, and Rho and
it extends the method slightly. The main results show that Malliavin calculus
allows a running Monte Carlo (MC) algorithm to present numerical
implementations and to illustrate its effectiveness. The main advantage of this
method is that once the algorithms are constructed, they can be used for
numerous types of option, even if their payoff functions are not
differentiable.
Explicit Solutions for Optimal Resource Extraction Problems under Regime Switching L\'evy Models
Date: 2018-06-15 00:00:00

This paper studies the problem of optimally extracting nonrenewable natural
resources. Taking into account the fact that the market values of the main
natural resources i.e. oil, natural gas, copper,..., etc, fluctuate randomly
following global and seasonal macroeconomic parameters, the prices of natural
resources are modeled using Markov switching L\'evy processes. We formulate
this optimal extraction problem as an infinite-time horizon optimal control
problem. We derive closed-form solutions for the value function as well as the
optimal extraction policy. Numerical examples are presented to illustrate these
results.