pwc-archive/papers-with-abstracts huggingface dataset:
Investigation of Phase Distortion on Perceived Speech Quality for Hearing-impaired Listeners
Date: 2020-07-29 00:00:00
Tasks: Speech Enhancement

Phase serves as a critical component of speech that influences the quality
and intelligibility. Current speech enhancement algorithms are beginning to
address phase distortions, but the algorithms focus on normal-hearing (NH)
listeners. It is not clear whether phase enhancement is beneficial for
hearing-impaired (HI) listeners. We investigated the influence of phase
distortion on speech quality through a listening study, in which NH and HI
listeners provided speech-quality ratings using the MUSHRA procedure. In one
set of conditions, the speech was mixed with babble noise at 4 different
signal-to-noise ratios (SNRs) from -5 to 10 dB. In another set of conditions,
the SNR was fixed at 10 dB and the noisy speech was presented in a simulated
reverberant room with T60s ranging from 100 to 1000 ms. The speech level was
kept at 65 dB SPL for NH listeners and amplification was applied for HI
listeners to ensure audibility. Ideal ratio masking (IRM) was used to simulate
speech enhancement. Two objective metrics (i.e., PESQ and HASQI) were utilized
to compare subjective and objective ratings. Results indicate that phase
distortion has a negative impact on perceived quality for both groups and PESQ
is more closely correlated with human ratings.
Uniqueness in Cauchy problems for diffusive real-valued strict local martingales
Authors: Umut Cetin, Kasper Larsen
Date: 2020-07-29 00:00:00

For a real-valued one dimensional diffusive strict local martingale,, we provide a set of smooth functions in which the Cauchy problem has a unique classical solution under a local H\"older condition. Under the weaker Engelbert-Schmidt conditions, we provide a set in which the Cauchy problem has a unique weak solution. We exemplify our results using quadratic normal volatility models and the two dimensional Bessel process.
Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry
Authors: Javier de Pedro-Carracedo, David Fuentes-Jimenez, Ana M. Ugena, Ana P. Gonzalez-Marcos
Date: 2020-07-29 00:00:00

In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biometric authentication system based on convolutional neural networks. Contrary to previous approaches, our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns image in the (p, q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal are strongly dependent on the vascular bed's biostructure, which is unique to each individual, and highly stable over time and other psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the convoluted nature of the blood network. Our biometric authentication system reaches very low Equal Error Rates (ERRs) with a single attempt, making it possible, by the very nature of the envisaged solution, to implement it in miniature components easily integrated into wearable biometric systems.
Robust Linear Estimation with Non-parametric Uncertainty: Average and Worst-case Performance (Full Version)
Authors: Gilberto O. Corrêa, Marlon M. López-Flores
Date: 2020-07-29 00:00:00

In this paper, two types of linear estimators are considered for three related estimation problems involving set-theoretic uncertainty pertaining to $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ balls of frequency-responses. The problems at stake correspond to robust $\mathcal{H}_{2}$ and $\mathcal{H}_{\infty}$ in the face of non-parametric "channel-model" uncertainty and to a nominal $\mathcal{H}_{\infty}$ estimation problem. The estimators considered here are defined by minimizing the worst-case squared estimation error over the "uncertainty set" and by minimizing an average cost under the constraint that the worst-case error of any admissible estimator does not exceed a prescribed value. The main point is to explore the derivation of estimators which may be viewed as less conservative alternatives to minimax estimators, or in other words, that allow for trade-offs between worst-case performance and better performance over "large" subsets of the uncertainty set. The "average costs" over $\mathcal{H}_{2}-$signal balls are obtained as limits of averages over sets of finite impulse responses, as their length grows unbounded. The estimator design problems for the two types of estimators and the three problems addressed here are recast as semi-definite programming problems (SDPs, for short). These SDPs are solved in the case of simple examples to illustrate the potential of the "average cost/worst-case constraint" estimators to mitigate the inherent conservatism of the minimax estimators.
Cold-atmospheric plasma induces tumor cell death in preclinical in vivo and in vitro models of human cholangiocarcinoma
Authors: Javier Vaquero, Florian Judée, Marie Vallette, Henri Decauchy, Ander Arbelaiz, Lynda Aoudjehane, Olivier Scatton, Ester Gonzalez-Sanchez, Fatiha Merabtene, Jérémy Augustin, Chantal Housset, Thierry Dufour, Laura Fouassier
Date: 2020-07-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Through the last decade, cold atmospheric plasma (CAP) has emerged as an innovative therapeutic option for cancer treatment. Recently, we have set up a potentially safe atmospheric pressure plasma jet device that displays antitumoral properties in a preclinical model of cholangiocarcinoma (CCA), a rare and very aggressive cancer emerging from the biliary tree with few efficient treatments. In the present study, we aimed at deciphering the molecular mechanisms underlying the antitumor effects of CAP towards CCA both in an in vivo and in vitro context. In vivo, using subcutaneous xenografts into immunocompromised mice, CAP treatment of CCA induced DNA lesions and tumor cell apoptosis, as evaluated by 8-oxoguanine and cleaved caspase-3 immunohistochemistry, respectively. Analysis of the tumor microenvironment showed changes in markers related to macrophage polarization. In vitro, incubation of CCA cells with CAP-treated culture media (i.e. plasma-activated media, PAM) led to a dose response decrease in cell survival. At molecular level, CAP treatment induced double-strand DNA breaks, followed by an increased phosphorylation and activation of the cell cycle master regulators CHK1 and p53, leading to cell cycle arrest and cell death by apoptosis. In conclusion, CAP is a novel therapeutic option to consider for CCA in the future.
Control Strategies for COVID-19 Epidemic with Vaccination, Shield Immunity and Quarantine: A Metric Temporal Logic Approach
Date: 2020-08-13 00:00:00

Ever since the outbreak of the COVID-19 epidemic, various public health
control strategies have been proposed and tested against the coronavirus
SARS-CoV-2. We study three specific COVID-19 epidemic control models: the
susceptible, exposed, infectious, recovered (SEIR) model with vaccination
control; the SEIR model with shield immunity control; and the susceptible,
un-quarantined infected, quarantined infected, confirmed infected (SUQC) model
with quarantine control. We express the control requirement in metric temporal
logic (MTL) formulas (a type of formal specification languages) which can
specify the expected control outcomes such as "the deaths from the infection
should never exceed one thousand per day within the next three months" or "the
population immune from the disease should eventually exceed 200 thousand within
the next 100 to 120 days". We then develop methods for synthesizing control
strategies with MTL specifications. To the best of our knowledge, this is the
first paper to systematically synthesize control strategies based on the
COVID-19 epidemic models with formal specifications. We provide simulation
results in three different case studies: vaccination control for the COVID-19
epidemic with model parameters estimated from data in Lombardy, Italy; shield
immunity control for the COVID-19 epidemic with model parameters estimated from
data in Lombardy, Italy; and quarantine control for the COVID-19 epidemic with
model parameters estimated from data in Wuhan, China. The results show that the
proposed synthesis approach can generate control inputs such that the
time-varying numbers of individuals in each category (e.g., infectious, immune)
satisfy the MTL specifications. The results also show that early intervention
is essential in mitigating the spread of COVID-19, and more control effort is
needed for more stringent MTL specifications.
Phenotypic variability in Italian rice germplasm
Authors: Gabriele Mongiano, Patrizia Titone, Simone Pagnoncelli, Davide Sacco, Luigi Tamborini, Roberto Pilu, Simone Bregaglio
Date: 2020-07-29 00:00:00

Plant breeding is one of the key strategies for further enhancement of crop yields; however, effective breeding strategies require phenotypic characterisation of the available germplasm. This study sought to characterise the phenotypic expression of fourteen crop traits related to phenology, plant architecture, and yield in a panel of 40 cultivars selected to represent the phenotypic variability present in Italian rice germplasm during a two-season field experiment. The observed range of phenotypic variation was high for many traits (coefficients of variation ranging from 5.9\% to 45.4\%) including yield (mean: 6.47 t ha\textsuperscript{-1}; CV: 15.4\%; min: 2.19 t ha\textsuperscript{-1}; max: 8.95 t ha\textsuperscript{-1}), and multiple strong associations emerged in all the analysed traits. Cluster analysis extracted three groups of genotypes characterised by alternative yield strategies, i.e. "high-tillering", "early maturing", and "increased source-sink". Our findings highlight that rice yield decrease when one strategy is overemphasised. In contrast, the highest-yielding genotypes have a balanced ratio between sink and source organs, along with a proportional duration of vegetative and reproductive phases. This study depicts the phenotypic variability in Italian rice cultivars and proposes a novel classification based on yield-related traits which could be of use in multiple rice breeding applications.
Deep Hedging of Long-Term Financial Derivatives
Authors: Alexandre Carbonneau
Date: 2020-07-29 00:00:00
Tasks: Benchmarking, Deep Reinforcement Learning, Management

This study presents a deep reinforcement learning approach for global hedging of long-term financial derivatives. A similar setup as in Coleman et al. (2007) is considered with the risk management of lookback options embedded in guarantees of variable annuities with ratchet features. The deep hedging algorithm of Buehler et al. (2019a) is applied to optimize neural networks representing global hedging policies with both quadratic and non-quadratic penalties. To the best of the author's knowledge, this is the first paper that presents an extensive benchmarking of global policies for long-term contingent claims with the use of various hedging instruments (e.g. underlying and standard options) and with the presence of jump risk for equity. Monte Carlo experiments demonstrate the vast superiority of non-quadratic global hedging as it results simultaneously in downside risk metrics two to three times smaller than best benchmarks and in significant hedging gains. Analyses show that the neural networks are able to effectively adapt their hedging decisions to different penalties and stylized facts of risky asset dynamics only by experiencing simulations of the financial market exhibiting these features. Numerical results also indicate that non-quadratic global policies are significantly more geared towards being long equity risk which entails earning the equity risk premium.
Third-Order Statistics Reconstruction from Compressive Measurements
Authors: Yanbo Wang, Zhi Tian
Date: 2020-07-30 00:00:00
Tasks: Compressive Sensing

Estimation of third-order statistics relies on the availability of a huge amount of data records, which can pose severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption, and unaffordably high sampling rate especially when dealing with high-dimensional data such as wideband signals. To overcome these challenges, this paper focuses on the reconstruction of the third-order cumulants under the compressive sensing framework. Specifically, this paper derives a transformed linear system that directly connects the cross-cumulants of compressive measurements to the desired third-order statistics. We provide sufficient conditions for lossless third-order statistics reconstruction via solving simple least-squares, along with the strongest achievable compression ratio. To reduce the computational burden, we also propose an approach to recover diagonal cumulant slices directly from compressive measurements, which is useful when the cumulant slices are sufficient for the inference task at hand. All the proposed techniques are tested via extensive simulations. The developed joint sampling and reconstruction approach to third-order statistics estimation is able to reduce the required sampling rates significantly by exploiting the cumulant structure resulting from signal stationarity, even in the absence of any sparsity constraints on the signal or cumulants.
Biogeography-Based Optimization and Support Vector Regression for Freeway Travel Time Prediction and Feature Selection
Authors: Prateek Bansal
Date: 2020-07-30 00:00:00
Tasks: feature selection, Prediction, regression

As travelers make their choices based on travel time, its prior information can be helpful for them in making more informed travel decisions. To achieve this goal, travel time prediction models have been proposed in literature, but identification of important predictors has not received much attention. Identification of important predictors reduces dimensions of input data, which not only lessens computational load, but also provides better understanding of underlying relationship between important predictors and travel time. Moreover, collection of only important predictors can lead to a significant equipment savings in data collection. Therefore, this study proposes a hybrid approach for feature selection (identifying important predictors) along with developing a robust freeway travel time prediction model. A framework integrating biogeography-based optimization (BBO) and support vector regression (SVR) has been developed. It was validated by predicting travel time at 36.1 km long segment of National Taiwan Freeway No. 1. The proposed hybrid approach is able to develop a prediction model with only six predictors, which is found to have accuracy equivalent to a stand-alone SVR prediction model developed with all forty three predictors.
Complex-valued Imaging with Total Variation Regularization: An Application to Full-Waveform Inversion in Visco-acoustic Media
Authors: Hossein S. Aghamiry, Ali Gholami, Stephane Operto
Date: 2020-07-30 00:00:00

Full waveform inversion (FWI) is a nonlinear PDE constrained optimization problem, which seeks to estimate constitutive parameters of a medium such as phase velocity, density, and anisotropy, by fitting waveforms. Attenuation is an additional parameter that needs to be taken into account in viscous media to exploit the full potential of FWI. Attenuation is more easily implemented in the frequency domain by using complex-valued velocities in the time-harmonic wave equation. These complex velocities are frequency-dependent to guarantee causality and account for dispersion. Since estimating a complex frequency-dependent velocity at each grid point in space is not realistic, the optimization is generally performed in the real domain by processing the phase velocity (or slowness) at a reference frequency and attenuation (or quality factor) as separate real parameters. This real parametrization requires an a priori empirical relation (such as the nonlinear Kolsky-Futterman (KF) or standard linear solid (SLS) attenuation models) between the complex velocity and the two real quantities, which is prone to generate modeling errors if it does not represent accurately the attenuation behavior of the subsurface. Moreover, it leads to a multivariate inverse problem, which is twice larger than the actual size of the medium and ill-posed due to the cross-talk between the two classes of real parameters. To alleviate these issues, we present a mono-variate algorithm that solves directly the optimization problem in the complex domain by processing in sequence narrow bands of frequencies under the assumption of band-wise frequency dependence of the sought complex velocities.
Dense Small Satellite Networks for Modern Terrestrial Communication Systems: Benefits, Infrastructure, and Technologies
Authors: Naveed UL Hassan, Chongwen Huang, Chau Yuen, Ayaz Ahmad, Yan Zhang
Date: 2020-07-30 00:00:00

Dense small satellite networks (DSSN) in low earth orbits (LEO) can benefit several mobile terrestrial communication systems (MTCS). However, the potential benefits can only be achieved through careful consideration of DSSN infrastructure and identification of suitable DSSN technologies. In this paper, we discuss several components of DSSN infrastructure including satellite formations, orbital paths, inter-satellite communication (ISC) links, and communication architectures for data delivery from source to destination. We also review important technologies for DSSN as well as the challenges involved in the use of these technologies in DSSN. Several open research directions to enhance the benefits of DSSN for MTCS are also identified in the paper. A case study showing the integration benefits of DSSN in MTCS is also included.
Novel Modelling and Control Strategies for a Steam Boiler under Fast Load Dynamics
Authors: Diego S. Carrasco, Graham C. Goodwin, Robert D. Peirce
Date: 2020-07-29 00:00:00

This paper describes a new nonlinear dynamic model for a natural circulation boiler. The model is based on physical principles, i.e. mass, energy and momentum balances. A systematic approach is followed leading to new insights into the physics of drum water level and downcomer mass flow. The model captures fast dynamic responses that are necessary to describe the operation of a boiler under highly variable load conditions. New features of the model include (i) a multi-compartment model for the risers, (ii) a new model for drum water level, and (iii) a new dynamic model for the flow of water in the downcomers. Implications of the model for control system design are explored in detail. Finally, the suggested improvements are validated in a sugar mill boiler.
A Practical Application of Sliding Mode Control in the Motion Control of a High Precision Piezoelectric Motor
Date: 2020-07-30 00:00:00
Tasks: Friction

This paper proposes a practical implementation of sliding mode control (SMC)
that utilizes partial modeling compensation. Sliding mode control is well known
for its effectiveness as a model free control approach, however, its
effectiveness is degraded if there is a constraint on the control gain or
limitation on the switching frequency in digital implementation. This is
especially the case with systems that involve static friction. This approach
aims to enhance the effectiveness of SMC by partial model compensation.
Rigorous stability proofs are presented to validate the approach. In addition,
experiments are carried out on a piezoelectric motor driven linear stage and
the control approach is compared with the Discrete-Time Integral Sliding Mode
(DTISMC) approach proposed by Abidi et al. as well as conventional PI control.
The results show that the proposed control approach has a superior performance
in comparison to the other approaches tested.
Endogenous and microbial volatile organic compounds in cutaneous health and disease
Authors: Emer Duffy, Aoife Morrin
Date: 2020-07-30 00:00:00
Tasks: Diagnostic

Human skin is a region of high metabolic activity where a rich variety of biomarkers are secreted from the stratum corneum. The skin is a constant source of volatile organic compounds (VOCs) derived from skin glands and resident microbiota. Skin VOCs contain the footprints of cellular activities and thus offer unique insights into the intricate processes of cutaneous physiology. This review examines the growing body of research on skin VOC markers as they relate to skin physiology, whereby variations in skin-intrinsic and microbial metabolic processes give rise to unique volatile profiles. Emerging evidence for volatile biomarkers linked to skin perturbations and skin cancer are examined. Microbial-derived VOCs are also investigated as prospective diagnostic markers, and their potential to shape the composition of the local skin microbiota, and consequently cutaneous health, is considered. Finally, a brief outlook on emerging analytical challenges and opportunities for skin VOC-based research and diagnostics is presented.
Signaling with Private Monitoring
Date: 2020-07-30 00:00:00

We study dynamic signaling when the informed party does not observe the
signals generated by her actions. A long-run player signals her type
continuously over time to a myopic second player who privately monitors her
behavior; in turn, the myopic player transmits his private inferences back
through an imperfect public signal of his actions. Preferences are
linear-quadratic and the information structure is Gaussian. We construct linear
Markov equilibria using belief states up to the long-run player's
$\textit{second-order belief}$. Because of the private monitoring, this state
is an explicit function of the long-run player's past play. A novel separation
effect then emerges through this second-order belief channel, altering the
traditional signaling that arises when beliefs are public. Applications to
models of leadership, reputation, and trading are examined.
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model
Authors: Carlo Campajola, Domenico Di Gangi, Fabrizio Lillo, Daniele Tantari
Date: 2020-07-30 00:00:00
Tasks: Time Series Analysis

A common issue when analyzing real-world complex systems is that the interactions between the elements often change over time: this makes it difficult to find optimal models that describe this evolution and that can be estimated from data, particularly when the driving mechanisms are not known. Here we offer a new perspective on the development of models for time-varying interactions introducing a generalization of the well-known Kinetic Ising Model (KIM), a minimalistic pairwise constant interactions model which has found applications in multiple scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology lets us significantly increase the knowledge that can be extracted from data using the simple KIM. In particular, we first identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics. Then we introduce a method to dynamically learn the value of such parameter from the data, without the need of specifying parametrically its dynamics. Finally, we extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time. We apply our methodology to several complex systems including financial markets, temporal (social) networks, and neuronal populations. Our results show that the Score-Driven KIM produces insightful descriptions of the systems, allowing to predict forecasting accuracy in real time as well as to separate different components of the dynamics. This provides a significant methodological improvement for data analysis in a wide range of disciplines.