pwc-archive/papers-with-abstracts huggingface dataset:
Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music
Authors: Yifu Sun, xulong Zhang, Yi Yu, Xi Chen, Wei Li
Date: 2020-04-08 00:00:00
Tasks: Information Retrieval, Melody Extraction, Music Information Retrieval, Retrieval

Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR). The task remains challenging since singing voice varies and intertwines with the accompaniment music, especially for some complicated polyphonic music such as choral music recordings. To address this problem, we investigate singing voice detection while discarding the interference from the accompaniment. The proposed SVD has two steps: i. The singing voice separation (SVS) technique is first utilized to filter out the singing voice's potential part coarsely. ii. Upon the continuity of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN) is used to learn compositional features. Moreover, to eliminate the outliers, we choose to use a median filter for time-domain smoothing. Experimental results show that the proposed method outperforms the existing state-of-the-art works on two public datasets, the Jamendo Corpus and the RWC pop dataset.
Correlating Lévy processes with Self-Decomposability: Applications to Energy Markets
Authors: Matteo Gardini, Piergiacomo Sabino, Emanuela Sasso
Date: 2020-04-08 00:00:00

Based on the concept of self-decomposability, we extend some recent multivariate L\'evy models built using multivariate subordination with the aim of capturing situations in which a sudden event in one market is propagated onto related markets after a certain stochastic time delay. Consequently, we study the properties of such processes, derive closed form expressions for the characteristic function and detail how a Monte Carlo scheme can be easily implemented. We illustrate the applicability of our approach in the context of gas and power Energy markets focusing on the calibration and on the pricing of spread options written on different underlying assets using simulations techniques.
Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics
Date: 2020-04-10 00:00:00

The infectious diseases are spreading due to human interactions enabled by
various social networks. Therefore, when a new pathogen such as SARS-CoV-2
causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social
distancing) are the only possible response to disrupt its spreading. To this
end, we introduce the new epidemic model (SICARS) and compare the centralized
(C), decentralized (D), and combined (C+D) social distancing strategies, and
analyze their efficiency to control the dynamics of COVID-19 on heterogeneous
complex networks. Our analysis shows that the centralized social distancing is
necessary to minimize the pandemic spreading. The decentralized strategy is
insufficient when used alone, but offers the best results when combined with
the centralized one. Indeed, the (C+D) is the most efficient isolation strategy
at mitigating the network superspreaders and reducing the highest node degrees
to less than 10% of their initial values. Our results also indicate that
stronger social distancing, e.g., cutting 75% of social ties, can reduce the
outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for
the (C+D) isolation strategy. Finally, we study the impact of proactive versus
reactive isolation strategies, as well as their delayed enforcement. We find
that the reactive response to the pandemic is less efficient, and delaying the
adoption of isolation measures by over one month (since the outbreak onset in a
region) can have alarming effects; thus, our study contributes to an
understanding of the COVID-19 pandemic both in space and time. We believe our
investigations have a high social relevance as they provide insights into
understanding how different degrees of social distancing can reduce the peak
infection ratio substantially; this can make the COVID-19 pandemic easier to
understand and control over an extended period of time.
Quantification of Risk in Classical Models of Finance
Authors: Alois Pichler, Ruben Schlotter
Date: 2020-04-09 00:00:00

This paper enhances the pricing of derivatives as well as optimal control problems to a level comprising risk. We employ nested risk measures to quantify risk, investigate the limiting behavior of nested risk measures within the classical models in finance and characterize existence of the risk-averse limit. As a result we demonstrate that the nested limit is unique, irrespective of the initially chosen risk measure. Within the classical models risk aversion gives rise to a stream of risk premiums, comparable to dividend payments. In this context we connect coherent risk measures with the Sharpe ratio from modern portfolio theory and extract the Z-spread -- a widely accepted quantity in economics to hedge risk. The results for European option pricing are then extended to risk-averse American options, where we study the impact of risk on the price as well as the optimal time to exercise the option. We also extend Merton's optimal consumption problem to the risk-averse setting.
Scenario analysis of non-pharmaceutical interventions on global COVID-19 transmissions
Authors: Xiaohui Chen, Ziyi Qiu
Date: 2020-04-07 00:00:00

This paper introduces a dynamic panel SIR (DP-SIR) model to investigate the impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission dynamics with panel data from 9 countries across the globe. By constructing scenarios with different combinations of NPIs, our empirical findings suggest that countries may avoid the lockdown policy with imposing school closure, mask wearing and centralized quarantine to reach similar outcomes on controlling the COVID-19 infection. Our results also suggest that, as of April 4th, 2020, certain countries such as the U.S. and Singapore may require additional measures of NPIs in order to control disease transmissions more effectively, while other countries may cautiously consider to gradually lift some NPIs to mitigate the costs to the overall economy.
Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI
Authors: Lei Zhang, Cunhua Pan, Yu Wang, Hong Ren, Kezhi Wang
Date: 2020-04-09 00:00:00

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) at the secondary user (SU) transmitter (ST) and phase shifts at the IRS to minimize the ST' s total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. The successive convex approximation (SCA) method, Schur's complement, General sign-definiteness principle, inverse Chi-square distribution and penalty convex-concave procedure are invoked for dealing with these intricate constraints. The non-convex optimization problems are transformed into several convex subproblems and efficient algorithms are proposed. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems. Simulation results also show that the number of transmit antennas at the ST and the number of phase shifts at the IRS should be carefully chosen to balance the channel realization feasibility rate and the total transmit power.
Efficiency of Covid-19 Mobile Contact Tracing Containment by Measuring Time Dependent Doubling Time
Date: 2020-07-29 00:00:00

The Covid-19 epidemic of the novel coronavirus (severe acute respiratory
syndrome SARS - CoV-2) has been spreading around the world. While different
containment policies using non-pharmaceutical interventions have been applied,
their efficiency are not known quantitatively. We show that the doubling time
Td(t) with the success s factor, the characteristic time of the exponential
growth of Td(t) in the arrested regime, is a reliable tool for early
predictions of epidemic spread time evolution and it provides a quantitative
measure of the success of different containment measures. The efficiency of the
containment policy Lockdown case Finding mobile Tracing (LFT) using mandatory
mobile contact tracing is much higher than the Lockdown Stop and Go (LSG)
policy proposed by the Imperial College team in London. A very low s factor was
reached by LFT policy giving the shortest time width of the dome of positive
case curve and the lowest number of fatalities. The LFT policy has been able to
reduce by a factor 100 the number of fatalities in the first 100 days of the
Covid-19 epidemic, to reduce the time width of the Covid-19 pandemic dome by a
factor 2.5 and to rapidly stop new outbreaks avoiding the second wave
Risk-Constrained Linear-Quadratic Regulators
Date: 2020-10-29 00:00:00

We propose a new risk-constrained reformulation of the standard Linear
Quadratic Regulator (LQR) problem. Our framework is motivated by the fact that
the classical (risk-neutral) LQR controller, although optimal in expectation,
might be ineffective under relatively infrequent, yet statistically significant
(risky) events. To effectively trade between average and extreme event
performance, we introduce a new risk constraint, which explicitly restricts the
total expected predictive variance of the state penalty by a user-prescribed
level. We show that, under rather minimal conditions on the process noise
(i.e., finite fourth-order moments), the optimal risk-aware controller can be
evaluated explicitly and in closed form. In fact, it is affine relative to the
state, and is always internally stable regardless of parameter tuning. Our new
risk-aware controller: i) pushes the state away from directions where the noise
exhibits heavy tails, by exploiting the third-order moment (skewness) of the
noise; ii) inflates the state penalty in riskier directions, where both the
noise covariance and the state penalty are simultaneously large. The properties
of the proposed risk-aware LQR framework are also illustrated via indicative
numerical examples.
The Benefits and Costs of Social Distancing in Rich and Poor Countries
Date: 2020-04-10 00:00:00

Social distancing is the primary policy prescription for combating the
COVID-19 pandemic, and has been widely adopted in Europe and North America. We
estimate the value of disease avoidance using an epidemiological model that
projects the spread of COVID-19 across rich and poor countries. Social
distancing measures that "flatten the curve" of the disease to bring demand
within the capacity of healthcare systems are predicted to save many lives in
high-income countries, such that practically any economic cost is worth
bearing. These social distancing policies are estimated to be less effective in
poor countries with younger populations less susceptible to COVID-19, and more
limited healthcare systems, which were overwhelmed before the pandemic.
Moreover, social distancing lowers disease risk by limiting people's economic
opportunities. Poorer people are less willing to make those economic
sacrifices. They place relatively greater value on their livelihood concerns
compared to contracting COVID-19. Not only are the epidemiological and economic
benefits of social distancing much smaller in poorer countries, such policies
may exact a heavy toll on the poorest and most vulnerable. Workers in the
informal sector lack the resources and social protections to isolate themselves
and sacrifice economic opportunities until the virus passes. By limiting their
ability to earn a living, social distancing can lead to an increase in hunger,
deprivation, and related mortality and morbidity. Rather than a blanket
adoption of social distancing measures, we advocate for the exploration of
alternative harm-reduction strategies, including universal mask adoption and
increased hygiene measures.
Scale invariance of cell size fluctuations in starving bacteria
Authors: Takuro Shimaya, Reiko Okura, Yuichi Wakamoto, Kazumasa A. Takeuchi
Date: 2020-04-10 00:00:00

In stable environments, cell size fluctuations are thought to be governed by simple physical principles, as suggested by recent findings of scaling properties. Here, by developing a microfluidic device and using E. coli, we investigate the response of cell size fluctuations against starvation. By abruptly switching to non-nutritious medium, we find that the cell size distribution changes but satisfies scale invariance: the rescaled distribution is kept unchanged and determined by the growth condition before starvation. These findings are underpinned by a model based on cell growth and cell cycle. Further, we numerically determine the range of validity of the scale invariance over various characteristic times of the starvation process, and find the violation of the scale invariance for slow starvation. Our results, combined with theoretical arguments, suggest the relevance of the multifork replication, which helps retaining information of cell cycle states and may thus result in the scale invariance.
A single-cell RNA expression map of coronavirus receptors and associated factors in developing human embryos
Authors: Stacy Colaco, Karisma Chhabria, Domdatt Singh, Anshul Bhide, Neha Singh, Abhishek Singh, Atahar Husein, Anuradha Mishra, Richa Sharma, Nancy Ashary, Deepak Modi
Date: 2020-04-10 00:00:00
Tasks: Management

To predict if developing human embryos are permissive to coronaviruses, we analyzed publicly available single cell RNA-seq datasets of zygotes, 4-cell, 8-cell, morula, inner cell mass, epiblast, primitive endoderm and trophectoderm for the coronavirus receptors (ACE2, BSG, DPP4 and ANPEP), the Spike protein cleavage enzymes (TMPRSS2, CTSL). We also analyzed the presence of host genes involved in viral replication, the endosomal sorting complexes required for transport (ESCRT) and SARS-Cov-2 interactions. The results reveal that ACE2, BSG, DPP4 and ANPEP are expressed in the cells of the zygote, to blastocyst including the trophectodermal lineage. ACE2, TMPRSS, BSG and CTSL are co-transcribed in a proportion of epiblast cells and most cells of the trophectoderm. The embryonic and trophectodermal cells also express genes for proteins ESCRT, viral replication and those that interact with SARS-CoV-2. We identified 1985 genes in epiblast and 1452 genes in the trophectoderm that are enriched in the ACE2 and TMPRSS2 co-expressing cells; 216 genes of these are common in both the cell types. These genes are associated with lipid metabolism, lysosome, peroxisome and oxidative phosphorylation pathways. Together our results suggest that developing human embryos could be permissive to coronavirus entry by both canonical and non-canonical mechanisms and they also express the genes for proteins involved in viral endocytosis and replication. This knowledge will be useful for evidence-based patient management for IVF during the COVID-19 pandemic.
Impact of intervention on the spread of COVID-19 in India: A model based study
Date: 2020-04-10 00:00:00

The outbreak of corona virus disease 2019 (COVID-19), caused by the virus
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already
created emergency situations in almost every country of the world. The disease
spreads all over the world within a very short period of time after its first
identification in Wuhan, China in December, 2019. In India, the outbreaks
starts on $2^{nd}$ March, 2020 and after that the cases are increasing
exponentially. Very high population density, the unavailability of specific
medicines or vaccines, insufficient evidences regarding the transmission
mechanism of the disease also make it difficult to fight against the disease
properly in India. Mathematical models have been used to predict the disease
dynamics and also to assess the efficiency of the intervention strategies in
reducing the disease burden. In this work, we propose a mathematical model to
describe the disease transmission mechanism between the individuals. We
consider the initial phase of the outbreak situation in India and our proposed
model is fitted to the daily cumulative new reported cases during the period
$2^{nd}$ March, 2020 to $24^{th}$ March, 2020. We estimate the basic
reproduction number $(R_0)$, effective reproduction number (R(t)) and epidemic
doubling time from the incidence data for the above-mentioned period. We
further assess the effect of preventive measures such as spread of awareness,
lock-down, proper hand sanitization, etc. in reducing the new cases. Two
intervention scenarios are considered depending on the variability of the
intervention strength over the period of implementation. Our study suggests
that higher intervention effort is required to control the disease outbreak
within a shorter period of time in India. Moreover, our analysis reveals that
the strength of the intervention should be strengthened over the time to
eradicate the disease effectively.
Forecasts with Bayesian vector autoregressions under real time conditions
Date: 2020-04-10 00:00:00
Tasks: Missing Values

This paper investigates the sensitivity of forecast performance measures to
taking a real time versus pseudo out-of-sample perspective. We use monthly
vintages for the United States (US) and the Euro Area (EA) and estimate a set
of vector autoregressive (VAR) models of different sizes with constant and
time-varying parameters (TVPs) and stochastic volatility (SV). Our results
suggest differences in the relative ordering of model performance for point and
density forecasts depending on whether real time data or truncated final
vintages in pseudo out-of-sample simulations are used for evaluating forecasts.
No clearly superior specification for the US or the EA across variable types
and forecast horizons can be identified, although larger models featuring TVPs
appear to be affected the least by missing values and data revisions. We
identify substantial differences in performance metrics with respect to whether
forecasts are produced for the US or the EA.
Deep Learning based OTDOA Positioning for NB-IoT Communication Systems
Authors: Guangjin Pan, Tao Wang, Xiufeng Jiang, Shunqing Zhang
Date: 2020-04-10 00:00:00
Tasks: Deep Learning

Positioning is becoming a key component in many Internet of Things (IoT) applications. The main challenges and limitations are the narrow bandwidth, low power and low cost which reduces the accuracy of the time of arrival (TOA) estimation. In this paper, we consider the positioning scenario of Narrowband IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA). By applying the deep learning based technique, we explore the generalization and feature extraction abilities of neural networks to tackle the aforementioned challenges. As demonstrated in the numerical experiments, the proposed algorithm can be used in different inter-site distance situations and results in a 15% and 50% positioning accuracy improvement compared with Gauss-Newton method in line-of-sight (LOS) scenario and non-line-of-sight (NLOS) scenario respectively.
On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation
Date: 2020-04-10 00:00:00

Balancing the model complexity and the representation capability towards the
process to be captured remains one of the main challenges in nonlinear system
identification. One possibility to reduce model complexity is to impose
structure on the model representation. To this end, this work considers the
linear fractional representation framework. In a linear fractional
representation the linear dynamics and the system nonlinearities are modeled by
two separate blocks that are interconnected with one another. This results in a
structured, yet flexible model structure. Estimating such a model directly from
input-output data is not a trivial task as the involved optimization is
nonlinear in nature. This paper proposes an initialization scheme for the model
parameters based on the best linear approximation of the system and shows that
this approach results in high quality models on a set of benchmark data sets.
Beyond Rescorla-Wagner: the ups and downs of learning
Authors: Gianluca Calcagni, Justin A. Harris, Ricardo Pellón
Date: 2020-04-10 00:00:00

We check the robustness of a recently proposed dynamical model of associative Pavlovian learning that extends the Rescorla-Wagner (RW) model in a natural way and predicts progressively damped oscillations in the response of the subjects. Using the data of two experiments, we compare the dynamical oscillatory model (DOM) with an oscillatory model made of the superposition of the RW learning curve and oscillations. Not only do data clearly show an oscillatory pattern, but they also favor the DOM over the added oscillation model, thus pointing out that these oscillations are the manifestation of an associative process. The latter is interpreted as the fact that subjects make predictions on trial outcomes more extended in time than in the RW model, but with more uncertainty.
Beamforming and precoding techniques
Date: 2020-04-10 00:00:00

Beamforming and precoding/combining are techniques aimed at processing
multiantenna signals at the transmitter and/or at the receiver of a wireless
communication system. While they have been routinely used to improve
performance in current and previous generations of mobile communications
systems, they are expected to play a more fundamental role in 5th Generation
(5G) New Radio (NR) cellular systems, whose functionalities have been defined
in the first phase of 3GPP 5G standardization process. Besides operating in
traditional cellular sub-6 GHz frequency band, 5G NR has been natively designed
also to work in the higher millimeter-wave (MMW) band. At lower frequencies,
multiantenna techniques for 5G NR are mainly refinements of those originally
designed for 4G Long Term Evolution (LTE). On the contrary, to cope with the
peculiarities of MMW scenarios, such as the larger number of antenna elements,
the more directional transmission, and the higher path loss values, new
dynamic, user-specific, and computationally-efficient multiantenna solutions
and procedures have been incorporated in 5G NR specifications. In particular,
since multiantenna techniques for 5G NR generally need detailed channel state
information (CSI), a complete redesign of the set of reference signals and
procedures used for CSI acquisition and reporting was carried out. 5G NR is
continuously evolving and new features will be added, while the existing ones
will be enhanced in the second phase of 5G standardization, with emphasis on
reduction of CSI overhead, robustness against spatial correlation among
channels, unconventional transmission methods, and software-based
reconfigurable antennas.
Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data
Authors: Carlos Lamarche, Thomas Parker
Date: 2020-04-10 00:00:00
Tasks: quantile regression, regression, valid

The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.