pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection
Date: 2019-04-18 00:00:00
Tasks: Fault Detection, Transfer Learning, Translation, Vocal Bursts Intensity Prediction

This letter presents a novel high impedance fault (HIF) detection approach
using a convolutional neural network (CNN). Compared to traditional artificial
neural networks, a CNN offers translation invariance and it can accurately
detect HIFs in spite of variance and noise in the input data. A transfer
learning method is used to address the common challenge of a system with little
training data. Extensive studies have demonstrated the accuracy and
effectiveness of using a CNNbased approach for HIF detection.
Quantification of Nematic Cell Polarity in Three-dimensional Tissues
Authors: André Scholich, Simon Syga, Hernán Morales-Navarrete, Fabián Segovia-Miranda, Hidenori Nonaka, Kirstin Meyer, Walter de Back, Lutz Brusch, Yannis Kalaidzidis, Marino Zerial, Frank Jülicher, Benjamin M. Friedrich
Date: 2019-04-18 00:00:00
Tasks: Open-Ended Question Answering

How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology. Here, we characterize a unique type of polarity found in liver tissue, nematic cell polarity, which is different from vectorial cell polarity in simple, sheet-like epithelia. We propose a conceptual and algorithmic framework to characterize complex patterns of polarity proteins on the surface of a cell in terms of a multipole expansion. To rigorously quantify previously observed tissue-level patterns of nematic cell polarity (Morales-Navarette et al., eLife 8:e44860, 2019), we introduce the concept of co-orientational order parameters, which generalize the known biaxial order parameters of the theory of liquid crystals. Applying these concepts to three-dimensional reconstructions of single cells from high-resolution imaging data of mouse liver tissue, we show that the axes of nematic cell polarity of hepatocytes exhibit local coordination and are aligned with the biaxially anisotropic sinusoidal network for blood transport. Our study characterizes liver tissue as a biological example of a biaxial liquid crystal. The general methodology developed here could be applied to other tissues or in-vitro organoids.
The impact of proportional transaction costs on systematically generated portfolios
Date: 2019-04-11 00:00:00
Tasks: Diversity

The effect of proportional transaction costs on systematically generated
portfolios is studied empirically. The performance of several portfolios (the
index tracking portfolio, the equally-weighted portfolio, the entropy-weighted
portfolio, and the diversity-weighted portfolio) in the presence of dividends
and transaction costs is examined under different configurations involving the
trading frequency, constituent list size, and renewing frequency. Moreover, a
method to smooth transaction costs is proposed.
Inefficiency of the Brazilian Stock Market: the IBOVESPA Future Contracts
Date: 2019-04-19 00:00:00

We present some indications of inefficiency of the Brazilian stock market
based on the existence of strong long-time cross-correlations with foreign
markets and indices. Our results show a strong dependence on foreign markets
indices as the S\&P 500 and CAC 40, but not to the Shanghai SSE 180, indicating
an intricate interdependence. We also show that the distribution of log-returns
of the Brazilian BOVESPA index has a discrete fat tail in the time scale of a
day, which is also a deviation of what is expected of an efficient equilibrated
market. As a final argument of the inefficiency of the Brazilian stock market,
we use a neural network approach to forecast the direction of movement of the
value of the IBOVESPA future contracts, with an accuracy allowing financial
returns over passive strategies.
ADOL - Markovian approximation of rough lognormal model
Authors: Peter Carr, Andrey Itkin
Date: 2019-04-19 00:00:00
Tasks: model

In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where the instantaneous variance is modelled by a lognormal process with drift and fractional diffusion. Since the DO process is a semi-martingale, it can be represented as an \Ito diffusion. It turns out that in this framework the process for the spot price $S_t$ is a geometric BM with stochastic instantaneous volatility $\sigma_t$, the process for $\sigma_t$ is also a geometric BM with stochastic speed of mean reversion and time-dependent colatility of volatility, and the supplementary process $\calV_t$ is the Ornstein-Uhlenbeck process with time-dependent coefficients, and is also a function of the Hurst exponent. We also introduce an adjusted DO process which provides a uniformly good approximation of the fractional BM for all Hurst exponents $H \in [0,1]$ but requires a complex measure. Finally, the characteristic function (CF) of $\log S_t$ in our model can be found in closed form by using asymptotic expansion. Therefore, pricing options and variance swaps (by using a forward CF) can be done via FFT, which is much easier than in rough volatility models.
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation
Authors: Yasin Yousif Al-Aboosi, Radhi Sehen Issa, Ali khalid Jassim
Date: 2019-04-19 00:00:00
Tasks: Denoising

In many applications, Image de-noising and improvement represent essential processes in presence of colored noise such that in underwater. Power spectral density of the noise is changeable within a definite frequency range, and autocorrelation noise function is does not like delta function. So, noise in underwater is characterized as colored noise. In this paper, a novel image de-noising method is proposed using multi-level noise power estimation in discrete wavelet transform with different basis functions. Peak signal to noise ratio (PSNR) and mean squared error represented performance measures that the results of this study depend on it. The results of various bases of wavelet such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that denoising process that uses in this method produces extra prominent images and improved values of PSNR than other methods.
Low Resolution Digital-to-Analog Converter with Digital Dithering for MIMO Transmitter
Date: 2019-04-19 00:00:00
Tasks: Quantization

Based on an equivalent model for quantizers with noisy inputs recently
presented in [35], we propose a method of digital dithering at the transmitter
that may significantly reduce the resolution requirements of MIMO downlink
Digital to Analog Convertors (DAC). We use this equivalent model to analyze the
effect of the dither Probability Density Function (PFD), and show that the
uniform PDF produces an optimal (linear) result. Relative to other methods of
DAC quantization error reduction our approach has the benefits of low
computational complexity, compatibility with all existing standards, and
blindness (no need for channel state information).
A Low Complexity Near-Maximum Likelihood MIMO Receiver with Low Resolution Analog-to-Digital Converters
Date: 2019-04-19 00:00:00

Based on a new equivalent model of quantizer with noisy input recently
presented in [23], we propose a new low complexity receiver that takes into
account the nonlinear distortion (NLD) generated by Analog to Digital converter
(ADC) with insufficient resolution. The strength of new model is that it
presents the NLD as a function of only the desired part of input signal
(without noise). Therefore it can easily be used in a variety of NLD mitigation
techniques. Here, as an illustration of this, we use a pseudo-ML approach to
detect the original QAM modulation based on the equivalent transfer function
and exhaustive search. Simulation results for a single user QAM under flat
fading show performance equivalent to a true ML receiver, but with much lower
computational complexity. The excellent performance of our receiver is an
independent validation of the model [23].
Deep Learning-Based Channel Estimation for High-Dimensional Signals
Date: 2019-04-19 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

We propose a novel deep learning-based channel estimation technique for
high-dimensional communication signals that does not require any training. Our
method is broadly applicable to channel estimation for multicarrier signals
with any number of antennas, and has low enough complexity to be used in a
mobile station. The proposed deep channel estimator can outperform LS
estimation with nearly the same complexity, and approach MMSE estimation
performance to within 1 dB without knowing the second order statistics. The
only complexity increase with respect to LS estimator lies in fitting the
parameters of a deep neural network (DNN) periodically on the order of the
channel coherence time. We empirically show that the main benefit of this
method accrues from the ability of this specially designed DNN to exploit
correlations in the time-frequency grid. The proposed estimator can also reduce
the number of pilot tones needed in an OFDM time-frequency grid, e.g. in an LTE
scenario by 98% (68%) when the channel coherence time interval is 73ms (4.5ms).
StegoAppDB: a Steganography Apps Forensics Image Database
Date: 2019-04-19 00:00:00
Tasks: Image Forensics, Image Steganography

In this paper, we present a new reference dataset simulating digital evidence
for image steganography. Steganography detection is a digital image forensic
topic that is relatively unknown in practical forensics, although stego app use
in the wild is on the rise. This paper introduces the first database consisting
of mobile phone photographs and stego images produced from mobile stego apps,
including a rich set of side information, offering simulated digital evidence.
StegoAppDB, a steganography apps forensics image database, contains over
810,000 innocent and stego images using a minimum of 10 different phone models
from 24 distinct devices, with detailed provenanced data comprising a wide
range of ISO and exposure settings, EXIF data, message information, embedding
rates, etc. We develop a camera app, Cameraw, specifically for data
acquisition, with multiple images per scene, saving simultaneously in both DNG
and high-quality JPEG formats. Stego images are created from these original
images using selected mobile stego apps through a careful process of reverse
engineering. StegoAppDB contains cover-stego image pairs including for apps
that resize the stego dimensions. We retainthe original devices and continue to
enlarge the database, and encourage the image forensics community to use
StegoAppDB. While designed for steganography, we discuss uses of this publicly
available database to other digital image forensic topics.
Horizon-unbiased Investment with Ambiguity
Date: 2019-04-20 00:00:00

In the presence of ambiguity on the driving force of market randomness, we
consider the dynamic portfolio choice without any predetermined investment
horizon. The investment criteria is formulated as a robust forward performance
process, reflecting an investor's dynamic preference. We show that the market
risk premium and the utility risk premium jointly determine the investors'
trading direction and the worst-case scenarios of the risky asset's mean return
and volatility. The closed-form formulas for the optimal investment strategies
are given in the special settings of the CRRA preference.
Height estimation for automotive MIMO radar with group-sparse reconstruction
Date: 2019-04-22 00:00:00

A method is developed for sequential azimuth and height estimation of small
objects at far distances in front of a moving vehicle using coherent or
mutually incoherent MIMO arrays. The model considers phases and amplitudes for
near-field multipath signals produced by specular non-diffusive
ground-reflections where the reflection phase shift and power attenuation due
to the interaction with the ground is assumed unknown. Group-sparsity allows
combining measurements along the trajectory of the vehicle provided that the
road is flat as well as measurements from multiple incoherent sensors at
different locations in the vehicle. It is shown in simulations that the
proposed approach significantly increases estimation accuracy and decreases
false alarms, both crucial for the detection of small objects at far distances.
This model is suitable for non-uniform sparse arrays and can be used for height
estimation using efficient methods such as block orthogonal matching pursuit.
Spatial Population Genetics: It's About Time
Date: 2019-05-10 00:00:00

Many questions that we have about the history and dynamics of organisms have
a geographical component: How many are there, and where do they live? How do
they move and interbreed across the landscape? How were they moving a thousand
years ago, and where were the ancestors of a particular individual alive today?
Answers to these questions can have profound consequences for our understanding
of history, ecology, and the evolutionary process. In this review, we discuss
how geographic aspects of the distribution, movement, and reproduction of
organisms are reflected in their pedigree across space and time. Because the
structure of the pedigree is what determines patterns of relatedness in modern
genetic variation, our aim is to thus provide intuition for how these processes
leave an imprint in genetic data. We also highlight some current methods and
gaps in the statistical toolbox of spatial population genetics.
ICT Capital-Skill Complementarity and Wage Inequality: Evidence from OECD Countries
Authors: Hiroya Taniguchi, Ken Yamada
Date: 2019-04-22 00:00:00

Although wage inequality has evolved in advanced countries over recent decades, it remains unknown the extent to which changes in wage inequality and their differences across countries are attributable to specific capital and labor quantities. We examine this issue by estimating a sector-level production function extended to allow for capital-skill complementarity and factor-biased technological change using cross-country and cross-industry panel data. Our results indicate that most of the changes in the skill premium are attributable to the relative quantities of ICT equipment, skilled labor, and unskilled labor in the goods and service sectors of the majority of advanced countries.
Optimal valuation of American callable credit default swaps under drawdown of L\'evy insurance risk process
Date: 2020-04-27 00:00:00

This paper discusses the valuation of credit default swaps, where default is
announced when the reference asset price has gone below certain level from the
last record maximum, also known as the high-water mark or drawdown. We assume
that the protection buyer pays premium at fixed rate when the asset price is
above a pre-specified level and continuously pays whenever the price increases.
This payment scheme is in favour of the buyer as she only pays the premium when
the market is in good condition for the protection against financial downturn.
Under this framework, we look at an embedded option which gives the issuer an
opportunity to call back the contract to a new one with reduced premium payment
rate and slightly lower default coverage subject to paying a certain cost. We
assume that the buyer is risk neutral investor trying to maximize the expected
monetary value of the option over a class of stopping time. We discuss optimal
solution to the stopping problem when the source of uncertainty of the asset
price is modelled by L\'evy process with only downward jumps. Using recent
development in excursion theory of L\'evy process, the results are given
explicitly in terms of scale function of the L\'evy process. Furthermore, the
value function of the stopping problem is shown to satisfy continuous and
smooth pasting conditions regardless of regularity of the sample paths of the
L\'evy process. Optimality and uniqueness of the solution are established using
martingale approach for drawdown process and convexity of the scale function
under Esscher transform of measure. Some numerical examples are discussed to
illustrate the main results.
Relation of the Weibull Shape Parameter with the Healthy Life Years Lost Estimates: Analytic Derivation and Estimation from an Extended Life Table
Date: 2019-04-23 00:00:00

Matsushita et al (1992) have done an interesting finding. They observed that
the shape parameter of the Weibull model presented systematic changes over time
and age when applied to mortality data for males and females in Japan. They
have also estimated that this parameter was smaller in the 1891-1898 data in
Japan compared to the 1980 mortality data and they presented an illustrative
figure for females where the values of the shape parameter are illustrated on
the diagram close to the corresponding survival curves. However, they have not
provided an analytical explanation of this behavior of the shape parameter of
the Weibull model. The cumulative hazard of this model can express the additive
process of applying a force in a material for enough time before cracking. To
pass to the human data, the Weibull model and the cumulative hazard can express
the additive process which disabilities and diseases cause the human organism
during the life span leading to healthy life years lost. In this paper we
further analytically derive a more general model of survival-mortality in which
we estimate a parameter related to the Healthy Life Years Lost (HLYL) and
leading to the Weibull model and the corresponding shape parameter as a
specific case. We have also demonstrated that the results found for the general
HLYL parameter we have proposed provides results similar to those provided by
the World Health Organization for the Healthy Life Expectancy (HALE) and the
corresponding HLYL estimates. An analytic derivation of the mathematical
formulas is presented along with an easy to apply Excel program. This program
is an extension of the classical life table including four more columns to
estimate the cumulative mortality, the average mortality, the person life years
lost and finally the HLYL parameter bx. The latest versions of this program
appear in the Demographics2019 website
Copula estimation for nonsynchronous financial data
Authors: Arnab Chakrabarti, Rituparna Sen
Date: 2019-04-23 00:00:00

Copula is a powerful tool to model multivariate data. We propose the modelling of intraday financial returns of multiple assets through copula. The problem originates due to the asynchronous nature of intraday financial data. We propose a consistent estimator of the correlation coefficient in case of Elliptical copula and show that the plug-in copula estimator is uniformly convergent. For non-elliptical copulas, we capture the dependence through Kendall's Tau. We demonstrate underestimation of the copula parameter and use a quadratic model to propose an improved estimator. In simulations, the proposed estimator reduces the bias significantly for a general class of copulas. We apply the proposed methods to real data of several stock prices.
Hedging longevity risk in defined contribution pension schemes
Authors: Ankush Agarwal, Christian-Oliver Ewald, Yongjie Wang
Date: 2019-04-23 00:00:00

Pension schemes all over the world are under increasing pressure to efficiently hedge the longevity risk posed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme which decides to hedge the longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme invests in the risky assets available in the market, including the longevity bond, by using the contributions from a representative scheme member to ensure a minimum guarantee such that the member is able to purchase a lifetime annuity upon retirement. We transform this constrained optimal investment problem into an unconstrained problem by replicating a self-financing portfolio of future contributions from the member and the minimum guarantee provided by the scheme. We solve the resulting optimisation problem using the dynamic programming principle and through a series of numerical studies reveal that the longevity risk has an important impact on the performance of investment strategies. Our results provide mathematical evidence supporting the use of mortality-linked securities for efficient hedging of the longevity risk.
Memory System Designed for Multiply-Accumulate (MAC) Engine Based on Stochastic Computing
Date: 2019-04-23 00:00:00

Convolutional neural network (CNN) achieves excellent performance on
fascinating tasks such as image recognition and natural language processing at
the cost of high power consumption. Stochastic computing (SC) is an attractive
paradigm implemented in low power applications which performs arithmetic
operations with simple logic and low hardware cost. However, conventional
memory structure designed and optimized for binary computing leads to extra
data conversion costs, which significantly decreases the energy efficiency.
Therefore, a new memory system designed for SC-based multiply-accumulate (MAC)
engine applied in CNN which is compatible with conventional memory system is
proposed in this paper. As a result, the overall energy consumption of our new
computing structure is 0.91pJ, which is reduced by 82.1% compared with the
conventional structure, and the energy efficiency achieves 164.8 TOPS/W.
Morphogenesis Software based on Epigenetic Code Concept
Authors: N. Bessonov, O. Butuzova, A. Minarsky, R. Penner, C. Soule, A. Tosenberger, N. Morozova
Date: 2019-04-23 00:00:00

The process of morphogenesis is an evolution of the shape of an organism together with the differentiation of its parts. This process encompasses numerous biological processes ranging from embryogenesis to regeneration following crisis such as amputation or transplantation. A fundamental theoretical question is where exactly do these instructions for (re-)construction reside and how are they implemented? We have recently proposed a set of concepts, aiming to respond to these questions and to provide an appropriate mathematical formalization of the geometry of morphogenesis [1]. First, we consider the possibility that evolution of shape is determined by epigenetic information, responsible for realization of different types of cell events. Second, we suggest a set of rules for converting this epigenetic information into instructive signals for cell events for each cell, as well as for transforming it after each cell event. Next we give notions of cell state, determined by its epigenetic array, and cell event, which is a change of cell state, and formalize development as a graph (tree) of cell states connected by 5 types of cell events, corresponding to the processes of cell division, cell growth, cell death, cell movement and cell differentiation. Here we present a Morphogenesis Software capable of simulating the evolution of a 3D embryo starting from zygote, following a set of rules based on our theoretical assumptions, and thus to provide a proof-of-concept for the hypothesis of epigenetic code regulation. The software creates a developing embryo and a corresponding graph of cell events according to the zygotic epigenetic spectrum and chosen parameters of the developmental rules. Variation of rules influencing the resulting shape of an embryo may help elucidating the principal laws underlying pattern formation.