pwc-archive/papers-with-abstracts huggingface dataset:
DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices
Date: 2020-08-15 00:00:00
Tasks: Speaker Recognition, Voice Conversion

With the recent advances in voice synthesis, AI-synthesized fake voices are
indistinguishable to human ears and widely are applied to produce realistic and
natural DeepFakes, exhibiting real threats to our society. However, effective
and robust detectors for synthesized fake voices are still in their infancy and
are not ready to fully tackle this emerging threat. In this paper, we devise a
novel approach, named \emph{DeepSonar}, based on monitoring neuron behaviors of
speaker recognition (SR) system, \ie, a deep neural network (DNN), to discern
AI-synthesized fake voices. Layer-wise neuron behaviors provide an important
insight to meticulously catch the differences among inputs, which are widely
employed for building safety, robust, and interpretable DNNs. In this work, we
leverage the power of layer-wise neuron activation patterns with a conjecture
that they can capture the subtle differences between real and AI-synthesized
fake voices, in providing a cleaner signal to classifiers than raw inputs.
Experiments are conducted on three datasets (including commercial products from
Google, Baidu, \etc) containing both English and Chinese languages to
corroborate the high detection rates (98.1\% average accuracy) and low false
alarm rates (about 2\% error rate) of DeepSonar in discerning fake voices.
Furthermore, extensive experimental results also demonstrate its robustness
against manipulation attacks (\eg, voice conversion and additive real-world
noises). Our work further poses a new insight into adopting neuron behaviors
for effective and robust AI aided multimedia fakes forensics as an inside-out
approach instead of being motivated and swayed by various artifacts introduced
in synthesizing fakes.
Non-concave expected utility optimization with uncertain time horizon
Authors: Christian Dehm, Thai Nguyen, Mitja Stadje
Date: 2020-05-28 00:00:00

We consider an expected utility maximization problem where the utility function is not necessarily concave and the time horizon is uncertain. We establish a necessary and sufficient condition for the optimality for general non-concave utility function in a complete financial market. We show that the general concavification approach of the utility function to deal with non-concavity, while being still applicable when the time horizon is a stopping time with respect to the financial market filtration, leads to sub-optimality when the time horizon is independent of the financial risk, and hence can not be directly applied. For the latter case, we suggest a recursive procedure which is based on the dynamic programming principle. We illustrate our findings by carrying out a multi-period numerical analysis for optimal investment problem under a convex option compensation scheme with random time horizon. We observe that the distribution of the non-concave portfolio in both certain and uncertain random time horizon is right-skewed with a long right tail, indicating that the investor expects frequent small losses and a few large gains from the investment. While the (certain) average time horizon portfolio at a premature stopping date is unimodal, the random time horizon portfolio is multimodal distributed which provides the investor a certain flexibility of switching between the local maximizers, depending on the market performance. The multimodal structure with multiple peaks of different heights can be explained by the concavification procedure, whereas the distribution of the time horizon has significant impact on the amplitude between the modes.
Equivalence between forward rate interpolations and discount factor interpolations for the yield curve construction
Authors: Jherek Healy
Date: 2020-05-28 00:00:00

The traditional way of building a yield curve is to choose an interpolation on discount factors, implied by the market tradable instruments. Since then, constructions based on specific interpolations of the forward rates have become the trend. We show here that some popular interpolation methods on the forward rates correspond exactly to classical interpolation methods on discount factors. This paper also aims at clarifying the differences between interpolations in terms of discount factors, instantaneous forward rates, discrete forward rates, and constant period forward rates.
On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules
Authors: Can Yang, Junjie Zhai, Helong Li
Date: 2020-05-19 00:00:00

Although there is a wide use of technical trading rules in stock markets, the profitability of them still remains controversial. This paper first presents and proves the upper bound of cumulative return, and then introduces many of conventional technical trading rules. Furthermore, with the help of bootstrap methodology, we investigate the profitability of technical trading rules on different international stock markets, including developed markets and emerging markets. At last, the results show that the technical trading rules are hard to beat the market, and even less profitable than the random trading strategy.
A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
Date: 2020-05-28 00:00:00
Proceeding: MIDL 2019 7
Tasks: MRI segmentation, Segmentation, Sensitivity

Brain MRI segmentation results should always undergo a quality control (QC)
process, since automatic segmentation tools can be prone to errors. In this
work, we propose two deep learning-based architectures for performing QC
automatically. First, we used generative adversarial networks for creating
error maps that highlight the locations of segmentation errors. Subsequently, a
3D convolutional neural network was implemented to predict segmentation
quality. The present pipeline was shown to achieve promising results and, in
particular, high sensitivity in both tasks.
Detection and Classification of Internal Faults in Power Transformers using Tree-based Classifiers
Authors: Samita Rani Pani, Pallav Kumar Bera, Vajendra Kumar
Date: 2020-05-28 00:00:00
Tasks: General Classification

This paper proposes a Decision Tree (DT) based detection and classification of internal faults in a power transformer. The faults are simulated in Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC) by varying the fault resistance, fault inception angle, and percentage of winding under fault. A series of features are extracted from the differential currents in phases a, b, and c belonging to the time, and frequency domains. Out of these, three features are selected to distinguish the internal faults from the magnetizing inrush and another three to classify faults in the primary and secondary of the transformer. DT, Random Forest (RF), and Gradient Boost (GB) classifiers are used to determine the fault types. The results show that DT detects faults with 100\% accuracy and the GB classifier performed the best among the three classifiers while classifying the internal faults.
A moment matching method for option pricing under stochastic interest rates
Authors: Fabio Antonelli, Alessandro Ramponi, Sergio Scarlatti
Date: 2020-05-28 00:00:00

In this paper we present a simple, but new, approximation methodology for pricing a call option in a Black \& Scholes market characterized by stochastic interest rates. The method, based on a straightforward Gaussian moment matching technique applied to a conditional Black \& Scholes formula, is quite general and it applies to various models, whether affine or not. To check its accuracy and computational time, we implement it for the CIR interest rate model correlated with the underlying, using the Monte Carlo simulations as a benchmark. The method's performance turns out to be quite remarkable, even when compared with analogous results obtained by the affine approximation technique presented in Grzelak and Oosterlee (2011) and by the expansion formula introduced in Kim and Kunimoto (1999), as we show in the last section.
A Vision to Smart Radio Environment: Surface Wave Communication Superhighways
Authors: Kai-Kit Wong, Kin-Fai Tong, Zhiyuan Chu, Yangyang Zhang
Date: 2020-05-28 00:00:00
Tasks: Management

Complementary to traditional approaches that focus on transceiver design for bringing the best out of unstable, lossy fading channels, one radical development in wireless communications that has recently emerged is to pursue a smart radio environment by using software-defined materials or programmable metasurfaces for establishing favourable propagation conditions. This article portraits a vision of communication superhighways enabled by surface wave (SW) propagation on "smart surfaces" for future smart radio environments. The concept differs from the mainstream efforts of using passive elements on a large surface for bouncing off radio waves intelligently towards intended user terminals. In this vision, energy efficiency will be ultra-high, due to much less pathloss compared to free space propagation, and the fact that SW is inherently confined to the smart surface not only greatly simplifies the task of interference management, but also makes possible exceptionally localized high-speed interference-free data access. We shall outline the opportunities and associated challenges arisen from the SW paradigm. We shall also attempt to shed light on several key enabling technologies that make this realizable. One important technology which will be discussed is a software-controlled fluidic waveguiding architecture that permits dynamic creation of high-throughput data highways.
On Sustainable Equilibria
Authors: Srihari Govindan, Rida Laraki, Lucas Pahl
Date: 2020-05-28 00:00:00

Following the ideas laid out in Myerson (1996), Hofbauer (2000) defined a Nash equilibrium of a finite game as sustainable if it can be made the unique Nash equilibrium of a game obtained by deleting/adding a subset of the strategies that are inferior replies to it. This paper proves two results about sustainable equilibria. The first concerns the Hofbauer-Myerson conjecture about the relationship between the sustainability of an equilibrium and its index: for a generic class of games, an equilibrium is sustainable iff its index is $+1$. Von Schemde and von Stengel (2008) proved this conjecture for bimatrix games; we show that the conjecture is true for all finite games. More precisely, we prove that an isolated equilibrium has index +1 if and only if it can be made unique in a larger game obtained by adding finitely many strategies that are inferior replies to that equilibrium. Our second result gives an axiomatic extension of sustainability to all games and shows that only the Nash components with positive index can be sustainable.
Amark: Automated Marking and Processing Techniques for Ambulatory ECG Data
Authors: Sharath Koorathota, Richard P. Sloan
Date: 2020-05-28 00:00:00
Tasks: Heart Rate Variability

We describe techniques and specifications of MATLAB software to process ambulatory electrocardiogram (ECG) data. Through template-based beat identification and simple pattern recognition models on the intervals between regular heart beats, we filter noisy sections of waveform and ectopic beats. Our end-to-end process can be used towards analysis of ECG and calculation of heart rate variability metrics after beat adjustments, removals and interpolation. Classification and noise detection is assessed on the human-annotated MIT-BIH Arrythmia and Noise Stress Test Databases.
On bid and ask side-specific tick sizes
Authors: Bastien Baldacci, Philippe Bergault, Joffrey Derchu, Mathieu Rosenbaum
Date: 2020-05-28 00:00:00

The tick size, which is the smallest increment between two consecutive prices for a given asset, is a key parameter of market microstructure. In particular, the behavior of high frequency market makers is highly related to its value. We take the point of view of an exchange and investigate the relevance of having different tick sizes on the bid and ask sides of the order book. Using an approach based on the model with uncertainty zones, we show that when side-specific tick sizes are suitably chosen, it enables the exchange to improve the quality of liquidity provision.
Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks
Date: 2020-05-28 00:00:00

The COVID-19 pandemic led several countries to resort to social distancing,
the only known way to slow down the spread of the virus and keep the health
system under control. Here we use an individual based model (IBM) to study how
the duration, start date and intensity of quarantine affect the height and
position of the peak of the infection curve. We show that stochastic effects,
inherent to the model dynamics, lead to variable outcomes for the same set of
parameters, making it crucial to compute the probability of each result. To
simplify the analysis we divide the outcomes in only two categories, that we
call {best and worst scenarios. Although long and intense quarantine is the
best way to end the epidemic, it is very hard to implement in practice. Here we
show that relatively short and intense quarantine periods can also be very
effective in flattening the infection curve and even killing the virus, but the
likelihood of such outcomes are low. Long quarantines of relatively low
intensity, on the other hand, can delay the infection peak and reduce its size
considerably with more than 50% probability, being a more effective policy than
complete lockdown for short periods.
Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S
Date: 2020-10-19 00:00:00
Tasks: counterfactual

This paper evaluates the dynamic impact of various policies adopted by US
states on the growth rates of confirmed Covid-19 cases and deaths as well as
social distancing behavior measured by Google Mobility Reports, where we take
into consideration people's voluntarily behavioral response to new information
of transmission risks. Our analysis finds that both policies and information on
transmission risks are important determinants of Covid-19 cases and deaths and
shows that a change in policies explains a large fraction of observed changes
in social distancing behavior. Our counterfactual experiments suggest that
nationally mandating face masks for employees on April 1st could have reduced
the growth rate of cases and deaths by more than 10 percentage points in late
April, and could have led to as much as 17 to 55 percent less deaths nationally
by the end of May, which roughly translates into 17 to 55 thousand saved lives.
Our estimates imply that removing non-essential business closures (while
maintaining school closures, restrictions on movie theaters and restaurants)
could have led to -20 to 60 percent more cases and deaths by the end of May. We
also find that, without stay-at-home orders, cases would have been larger by 25
to 170 percent, which implies that 0.5 to 3.4 million more Americans could have
been infected if stay-at-home orders had not been implemented. Finally, not
having implemented any policies could have led to at least a 7 fold increase
with an uninformative upper bound in cases (and deaths) by the end of May in
the US, with considerable uncertainty over the effects of school closures,
which had little cross-sectional variation.
Pricing Temperature Derivatives under a Time-Changed Levy Model
Authors: Pablo Olivares
Date: 2020-05-29 00:00:00

The objective of the paper is to price weather contracts using temperature as the underlying process when the later follows a mean-reverting dynamics driven by a time-changed Brownian motion coupled to a Gamma Levy subordinator and time-dependent deterministic volatility. This type of model captures the complexity of the temperature dynamic providing a more accurate valuation of their associate weather contracts. An approximated price is obtained by a Fourier expansion of its characteristic function combined with a selection of the equivalent martingale measure following the Esscher transform proposed in Gerber and Shiu (1994).
Digital twin, physics-based model, and machine learning applied to damage detection in structures
Authors: TG Ritto, FA Rochinha
Date: 2020-05-29 00:00:00
Tasks: BIG-bench Machine Learning, Prognosis

This work is interested in digital twins, and the development of a simplified framework for them, in the context of dynamical systems. Digital twin is an ingenious concept that helps on organizing different areas of expertise aiming at supporting engineering decisions related to a specific asset; it articulates computational models, sensors, learning, real time analysis, diagnosis, prognosis, and so on. In this framework, and to leverage its capacity, we explore the integration of physics-based models with machine learning. A digital twin is constructed for a damaged structure, where a discrete physics-based computational model is employed to investigate several damage scenarios. A machine learning classifier, that serves as the digital twin, is trained with data taken from a stochastic computational model. This strategy allows the use of an interpretable model (physics-based) to build a fast digital twin (machine learning) that will be connected to the physical twin to support real time engineering decisions. Different classifiers (quadratic discriminant, support vector machines, etc) are tested, and different model parameters (number of sensors, level of noise, damage intensity, uncertainty, operational parameters, etc) are considered to construct datasets for the training. The accuracy of the digital twin depends on the scenario analyzed. Through the chosen application, we are able to emphasize each step of a digital twin construction, including the possibility of integrating physics-based models with machine learning. The different scenarios explored yield conclusions that might be helpful for a large range of applications.
Pricing Energy Contracts under Regime Switching Time-Changed models
Authors: Konrad Gajewski, Sebastian Ferrando, Pablo Olivares
Date: 2020-05-29 00:00:00

The shortcomings of the popular Black-Scholes-Merton (BSM) model have led to models which could more accurately model the behavior of the underlying assets in energy markets, particularly in electricity and future oil prices. In this paper we consider a class of regime switching time-changed Levy processes, which builds upon the BSM model by incorporating jumps through a random clock, as well as randomly varying parameters according to a two-state continuous-time Markov chain. We implement pricing methods based on expansions of the characteristic function as in \cite{Fourier}. Finally, we estimate the parameters of the model by incorporating historic energy data and option quotes using a variety of methods.
Complexity Reduction of Volterra Nonlinear Equalization for Optical Short-Reach IM/DD Systems
Authors: Tom Wettlin, Talha Rahman, Jinlong Wei, Stefano Calabrò, Nebojsa Stojanovic, Stephan Pachnicke
Date: 2020-05-29 00:00:00

We investigate approaches to reduce the computational complexity of Volterra nonlinear equalizers (VNLEs) for short-reach optical transmission systems using intensity modulation and direct detection (IM/DD). In this contribution we focus on a structural reduction of the number of kernels, i.e. we define rules to decide which terms need to be implemented and which can be neglected before the kernels are calculated. This static complexity reduction is to be distinguished from other approaches like pruning or L1 regularization, that are applied after the adaptation of the full Volterra equalizer e.g. by thresholding. We investigate the impact of the complexity reduction on 90 GBd PAM6 IM/DD experimental data acquired in a back-to-back setup as well as in case of transmission over 1 km SSMF. First, we show, that the third-order VNLE terms have a significant impact on the overall performance of the system and that a high number of coefficients is necessary for optimal performance. Afterwards, we show that restrictions, for example on the tap spacing among samples participating in the same kernel, can lead to an improved tradeoff between performance and complexity compared to a full third-order VNLE. We show an example, in which the number of third-order kernels is halved without any appreciable performance degradation.
A new SEIR type model including quarantine effects and its application to analysis of Covid-19 pandemia in Poland in March-April 2020
Date: 2020-05-29 00:00:00

Contact tracing and quarantine are well established non-pharmaceutical
epidemic control tools. The paper aims to clarify the impact of these measures
in COVID-19 epidemic. A new deterministic model is introduced (SEIRQ:
susceptible, exposed, infectious, removed, quarantined) with Q compartment
capturing individuals and releasing them with delay. We obtain a simple rule
defining the reproduction number $\mathcal{R}$ in terms of quarantine
parameters, ratio of diagnosed cases and transmission parameters. The model is
applied to the epidemic in Poland in March - April 2020, when social distancing
measures were in place. We investigate 3 scenarios corresponding to different
ratios of diagnosed cases. Our results show that depending on the scenario
contact tracing could have prevented from 50\% to over 90\% of cases. The
effects of quarantine are limited by fraction of undiagnosed cases. Taking into
account the transmission intensity in Poland prior to introduction of social
restrictions it is unlikely that the control of the epidemic could be achieved
without any social distancing measures.
A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy
Date: 2020-05-28 00:00:00

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health
of humans and causes great economic losses. Predictive modelling and
forecasting the epidemic trends are essential for developing countermeasures to
mitigate this pandemic. We develop a network model, where each node represents
an individual and the edges represent contacts between individuals where the
infection can spread. The individuals are classified based on the number of
contacts they have each day (their node degrees) and their infection status.
The transmission network model was respectively fitted to the reported data for
the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian
Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our
model fits all three regions well with narrow confidence intervals and could be
adapted to simulate other megacities or regions. The model projections on the
role of containment strategies can help inform public health authorities to
plan control measures.
Adaptive Switching Control of Wind Turbine Generators for Necessary Frequency Response
Date: 2020-05-29 00:00:00

This letter proposes a new control strategy for wind turbine generators to
decide the necessity of switches between the normal operation and frequency
support modes. The idea is to accurately predict an unsafe frequency response
using a differential transformation method right after power imbalance is
detected so as to adaptively activate a frequency support mode only when
necessary. This control strategy can effectively avoid unnecessary switches
with a conventional use of deadband but still ensure adequate frequency
response.