pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of a Stochastic COVID-19 Epidemic Model with Jump-Diffusion
Authors: Almaz Tesfay, Tareq Saeed, Anwar Zeb, Daniel Tesfay, Anas Khalaf, James Brannan
Date: 2020-11-12 00:00:00

For a stochastic COVID-19 model with jump-diffusion, we prove the existence and uniqueness of the global positive solution. We also investigate some conditions for the extinction and persistence of the disease. We calculate the threshold of the stochastic epidemic system which determines the extinction or permanence of the disease at different intensities of the stochastic noises. This threshold is denoted by $\xi$ which depends on the white and jump noises. The effects of these noises on the dynamics of the model are studied. The numerical experiments show that the random perturbation introduced in the stochastic model suppresses disease outbreaks as compared to its deterministic counterpart. In other words, the impact of the noises on the extinction and persistence is high. When the noise is large or small, our numerical findings show that the COVID-19 vanishes from the population if $\xi <1;$ whereas the epidemic can't go out of control if $\xi >1.$ From this, we observe that white noise and jump noise have a significant effect on the spread of COVID-19 infection, i.e., we can conclude that the stochastic model is more realistic than the deterministic one. Finally, to illustrate this phenomenon, we put some numerical simulations.
Generating unfavourable VaR scenarios with patchwork copulas
Authors: Dietmar Pfeifer, Olena Ragulina
Date: 2020-11-12 00:00:00

The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union. The method is exemplified with a 19-dimensional real-life data set of insurance losses.
COVID-Town: An Integrated Economic-Epidemiological Agent-Based Model
Authors: Patrick Mellacher
Date: 2020-11-12 00:00:00
Tasks: counterfactual

I develop a novel macroeconomic epidemiological agent-based model to study the impact of the COVID-19 pandemic under varying policy scenarios. Agents differ with regard to their profession, family status and age and interact with other agents at home, work or during leisure activities. The model allows to implement and test actually used or counterfactual policies such as closing schools or the leisure industry explicitly in the model in order to explore their impact on the spread of the virus, and their economic consequences. The model is calibrated with German statistical data on time use, demography, households, firm demography, employment, company profits and wages. I set up a baseline scenario based on the German containment policies and fit the epidemiological parameters of the simulation to the observed German death curve and an estimated infection curve of the first COVID-19 wave. My model suggests that by acting one week later, the death toll of the first wave in Germany would have been 180% higher, whereas it would have been 60% lower, if the policies had been enacted a week earlier. I finally discuss two stylized fiscal policy scenarios: procyclical (zero-deficit) and anticyclical fiscal policy. In the zero-deficit scenario a vicious circle emerges, in which the economic recession spreads from the high-interaction leisure industry to the rest of the economy. Even after eliminating the virus and lifting the restrictions, the economic recovery is incomplete. Anticyclical fiscal policy on the other hand limits the economic losses and allows for a V-shaped recovery, but does not increase the number of deaths. These results suggest that an optimal response to the pandemic aiming at containment or holding out for a vaccine combines early introduction of containment measures to keep the number of infected low with expansionary fiscal policy to keep output in lower risk sectors high.
Hardware Complexity Aware Design Strategy for a Fused Logarithmic and Anti-Logarithmic Converter
Authors: Botao Xiong, Yuanfeng Sui
Date: 2020-11-12 00:00:00

The logarithmic and anti-logarithmic converters are realized with the piecewise linear approximation method, which is implemented by the shift-and-add architecture. This brief utilizes the similarities of Log and Antilog functions so that the adder tree block and multiplexer block can be shared by the Log and Antilog converters. As a result, the Antilog function can be implemented by the Log converter at the cost of additional 14% area and 6% latency. It implies the shift-and-add architecture can approximate multiple similar nonlinear functions with a slightly hardware cost. In addition, this brief proposes a set of formulas to predict the area and latency of shift-and-add architecture with different quantized coefficients that can facilitate the finding of a trade-off point in the Latency-Area-Precision space.
Curved Holographic Optical Elements from a Geometric View Point
Authors: Tobias Graf
Date: 2020-10-23 00:00:00

We present a geometric framework to model the optical effects of deformations of planar holographic optical elements (HOE) into curved surfaces, such as sphere segments. In particular, we consider deformations which do not preserve the Gaussian curvature.
First steps toward a simple but efficient model-free control synthesis for variable-speed wind turbines
Date: 2020-11-12 00:00:00

Although variable-speed three-blade wind turbines are nowadays quite popular,
their control remains a challenging task. We propose a new easily implementable
model-free control approach with the corresponding intelligent controllers.
Several convincing computer simulations, including some fault accommodations,
shows that model-free controllers are more efficient and robust than classic
proportional-integral controllers.
Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions
Authors: Richard Spady, Sami Stouli
Date: 2020-11-12 00:00:00
Tasks: Econometrics, regression

We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible Maximum Likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework.
Secure Identification for Gaussian Channels and Identification for Multi-Antenna Gaussian Channels
Authors: Wafa Labidi, Christian Deppe, Holger Boche
Date: 2020-11-12 00:00:00

New applications in modern communications are demanding robust and ultra-reliable low latency information exchange such as machine-to-machine and human-to-machine communications. For many of these applications, the identification approach of Ahlswede and Dueck is much more efficient than the classical message transmission scheme proposed by Shannon. Previous studies concentrate mainly on identification over discrete channels. For discrete channels, it was proved that identification is robust under channels uncertainty. Furthermore, optimal identification schemes that are secure and robust against jamming attacks have been considered. However, no results for continuous channels have yet been established. That is why we focus on the continuous case: the Gaussian channel for its known practical relevance. We deal with secure identification over Gaussian channels. Provable secure communication is of a high interest for future communication systems. A key technique for implementing secure communication is the physical layer security based on information theoretic security. We model this with the wiretap channel. In particular, we provide a suitable coding scheme for the Gaussian wiretap channel (GWC) and determine the corresponding secure identification capacity. We also consider Multiple-Input Multiple-Output (MIMO) Gaussian channels and provide an efficient signal-processing scheme. This scheme allows a separation of signal-processing and Gaussian coding.
Master regulators as order parameters of gene expression states
Authors: Andreas Krämer
Date: 2020-11-12 00:00:00

Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model. It is shown that order parameters of this model can be interpreted as concentrations of master transcription regulators that form concurrent positive feedback loops with a large number of downstream regulated genes. The order parameter free energy then defines an epigenetic landscape in which local minima correspond to stable cell states. The model is applied to gene expression data in the context of hematopoiesis.
Prospects and challenges of quantum finance
Authors: Adam Bouland, Wim van Dam, Hamed Joorati, Iordanis Kerenidis, Anupam Prakash
Date: 2020-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Portfolio Optimization, Quantum Machine Learning

Quantum computers are expected to have substantial impact on the finance industry, as they will be able to solve certain problems considerably faster than the best known classical algorithms. In this article we describe such potential applications of quantum computing to finance, starting with the state-of-the-art and focusing in particular on recent works by the QC Ware team. We consider quantum speedups for Monte Carlo methods, portfolio optimization, and machine learning. For each application we describe the extent of quantum speedup possible and estimate the quantum resources required to achieve a practical speedup. The near-term relevance of these quantum finance algorithms varies widely across applications - some of them are heuristic algorithms designed to be amenable to near-term prototype quantum computers, while others are proven speedups which require larger-scale quantum computers to implement. We also describe powerful ways to bring these speedups closer to experimental feasibility - in particular describing lower depth algorithms for Monte Carlo methods and quantum machine learning, as well as quantum annealing heuristics for portfolio optimization. This article is targeted at financial professionals and no particular background in quantum computation is assumed.
Evaluating the Intelligibility Benefits of Neural Speech Enrichment for Listeners with Normal Hearing and Hearing Impairment using the Greek Harvard Corpus
Date: 2020-11-12 00:00:00
Tasks: Speech Enhancement

In this work we evaluate a neural based speech intelligibility booster based
on spectral shaping and dynamic range compression (SSDRC), referred to as
WaveNet-based SSDRC (wSSDRC), using a recently designed Greek Harvard-style
corpus. The corpus has been developed according to the format of the
Harvard/IEEE sentences and offers the opportunity to apply neural speech
enhancement models and examine their performance gain for Greek listeners.
wSSDRC has been successfully tested for English material and speakers in the
past. In this paper we revisit wSSDRC to perform a full scale evaluation of the
model with Greek listeners under the condition of equal energy before and after
modification. Both normal hearing (NH) and hearing impaired (HI) listeners
evaluated the model under speech shaped noise (SSN) at listener-specific SNRs
matching their Speech Reception Threshold (SRT) - a point at which 50 % of
unmodified speech is intelligible. The analysis statistics show that the wSSDRC
model has produced a median intelligibility boost of 39% for NH and 38% for HI,
relative to the plain unprocessed speech.
Shadow economy and populism-risk and uncertainty factors for establishing low-carbon economy of Balkan countries-case study for Bulgaria
Authors: Shteryo Nozharov, Nina Nikolova
Date: 2020-11-13 00:00:00

The main purpose of the current publication is to formulate a scenario model for analysis of the opportunities for low-carbon economy establishment in the countries with transition economies.The model studies risk factors such as shadow economy level and populism based on the implementation and development of Balkan countries economic policy and at the same time shows future climate changes tendencies and uncertainties of climate models.A transdisciplinary approach is implemented in the study. Climate change perception and understanding about low-carbon economy are examined through the public opinion and analysis of mass-media publications.The results of the research are important in order to clarify the multicultural divergences as a factor for risk and uncertainty in the implementation process of the policy for climate change.In this way geographical aspects of risk and uncertainty, which are not only related to the economic development of the relevant countries, could be brought out.
A comprehensive mathematical model of a low-friction servopneumatic actuator
Date: 2020-11-12 00:00:00
Tasks: Friction

This paper presents a comprehensive mathematical model of a servopneumatic
system, aimed at its consolidation in literature. The work exploits system's
friction forces, temperature and pressure evolution, heat transfer, leakage
between chambers and environment, equilibrium of cylinder forces, resistance of
the pipes, mass flow rate in valve output, varying area of valve orifices and
equilibrium of valve forces. Numerical simulations are performed, where
system's elastic behaviour due to the air compressibility is thoroughly
exploited.
Mathematical Model of Hippocampal Microdialysis: Validation of in vivo Methodology
Authors: Damon Vinciguerra, Margot Vigeant, Ewan C. McNay
Date: 2020-11-12 00:00:00
Tasks: valid

Microdialysis is a well-established method for in vivo neurochemical measurements of small molecules, with implanted concentric-design probes offering minimized tissue damage and good temporal and spatial resolution. However, the large majority of measurements do not allow the perfusate to reach equilibrium with the brain, so that inferential methods of sample concentration correction such as zero-net-flux must be used to determine actual brain extracellular fluid glucose concentrations. In order for such methods to be valid, steady-state transfer of the analyte of interest within the brain is required, but this situation has not previously been confirmed. A first-principles mathematical model of fluid flow and analyte diffusion around an implanted microdialysis probe was developed and implemented in COMSOL in order to validate the zero-net-flux approach, using measurement of extracellular brain glucose levels as a well-explored example system against which to compare the model. Results from the model accurately reproduced and predicted results from in vivo experiments. Importantly, the model predicts that the time for an implanted probe to achieve steady-state equilibrium with the surrounding extracellular fluid is on the order of one to two minutes, supporting the validity of this technique for quantitative measurement of in vivo neurochemistry.
Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors
Authors: Diaa Badawi, Agamyrat Agambayev, Sule Ozev, A. Enis Cetin
Date: 2020-11-12 00:00:00

Sensor drift is a major problem in chemical sensors that requires addressing for reliable and accurate detection of chemical analytes. In this paper, we develop a causal convolutional neural network (CNN) with a Discrete Cosine Transform (DCT) layer to estimate the drift signal. In the DCT module, we apply soft-thresholding nonlinearity in the transform domain to denoise the data and obtain a sparse representation of the drift signal. The soft-threshold values are learned during training. Our results show that DCT layer-based CNNs are able to produce a slowly varying baseline drift signal. We train the CNN on synthetic data and test it on real chemical sensor data. Our results show that we can have an accurate and smooth drift estimate even when the observed sensor signal is very noisy.
When Should We (Not) Interpret Linear IV Estimands as LATE?
Authors: Tymon Słoczyński
Date: 2020-11-13 00:00:00

In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a situation in which additional covariates are required for identification while the reduced-form and first-stage regressions may be misspecified due to an implicit homogeneity restriction on the effects of the instrument. I show that the weights on some conditional LATEs are negative and the IV estimand is no longer interpretable as a causal effect under a weaker version of monotonicity, i.e. when there are compliers but no defiers at some covariate values and defiers but no compliers elsewhere. The problem of negative weights disappears in the interacted specification of Angrist and Imbens (1995), which avoids misspecification and seems to be underused in applied work. I illustrate my findings in an application to the causal effects of pretrial detention on case outcomes. In this setting, I reject the stronger version of monotonicity, demonstrate that the interacted instruments are sufficiently strong for consistent estimation using the jackknife methodology, and present several estimates that are economically and statistically different, depending on whether the interacted instruments are used.
Network Topology Invariant Stability Certificates for DC Microgrids with Arbitrary Load Dynamics
Authors: Samuel Chevalier, Federico Martin Ibanez, Kathleen Cavanagh, Konstantin Turitsyn, Luca Daniel, Petr Vorobev
Date: 2020-11-13 00:00:00

DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads. This paper develops a decentralized stability certificate which is capable of certifying the small-signal stability of an islanded DC network containing such loads. Utilizing a novel homotopy approach, the proposed standards ensure that no system eigenmodes are able to cross into the unstable right half plane for a continuous range of controller gain levels. The resulting "standards" can be applied to variety of grid components which meet the specified, but non-unique, criteria. These standards thus take a step towards offering plug-and-play operability of DC microgrids. The proposed theorems are explicitly illustrated and numerically validated on multiple DC microgrid test-cases containing both buck and boost converter dynamics.
Community detection in network neuroscience
Date: 2020-11-13 00:00:00
Tasks: Community Detection

Many real-world networks, including nervous systems, exhibit meso-scale
structure. This means that their elements can be grouped into meaningful
sub-networks. In general, these sub-networks are unknown ahead of time and must
be "discovered" algorithmically using community detection methods. In this
article, we review evidence that nervous systems exhibit meso-scale structure
in the form of communities, clusters, and modules. We also provide a set of
guidelines to assist users in applying community detection methods to their own
network data. These guidelines focus on the method of modularity maximization
but, in many cases, are general and applicable to other techniques.
Weak Identification in Discrete Choice Models
Authors: David T. Frazier, Eric Renault, Lina Zhang, Xueyan Zhao
Date: 2020-11-13 00:00:00
Tasks: Discrete Choice Models

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Furthermore, we compare our approach against those commonly applied in the literature in two empirical examples: married women labor force participation, and US food aid and civil conflicts.
Stochastic stability of agglomeration patterns in an urban retail model
Authors: Minoru Osawa, Takashi Akamatsu, Yosuke Kogure
Date: 2020-11-13 00:00:00

We consider a model of urban spatial structure proposed by Harris and Wilson (Environment and Planning A, 1978). The model consists of fast dynamics, which represent spatial interactions between locations by the entropy-maximizing principle, and slow dynamics, which represent the evolution of the spatial distribution of local factors that facilitate such spatial interactions. One known limitation of the Harris and Wilson model is that it can have multiple locally stable equilibria, leading to a dependence of predictions on the initial state. To overcome this, we employ equilibrium refinement by stochastic stability. We build on the fact that the model is a large-population potential game and that stochastically stable states in a potential game correspond to global potential maximizers. Unlike local stability under deterministic dynamics, the stochastic stability approach allows a unique and unambiguous prediction for urban spatial configurations. We show that, in the most likely spatial configuration, the number of retail agglomerations decreases either when shopping costs for consumers decrease or when the strength of agglomerative effects increases.
Global warNing: challenges, threats and opportunities for ground beetles (Coleoptera: Carabidae) in high altitude habitats
Date: 2020-11-13 00:00:00

Aim of this paper is to provide the first comprehensive synthesis about
ground beetle (Coleoptera: Carabidae) distribution in high altitude habitats.
Specifically, the attention is focused on the species assemblages living on the
most common ice-related mountain landforms (glaciers, debris-covered glaciers,
glacier forelands and rock glaciers) and on the challenges, threats and
opportunities carabids living in these habitats have to face in relation to the
ongoing climate warming. The suggested role of the ice-related alpine
landforms, as present climatic refugia for cold-adapted ground beetles, is
discussed. Finally, the needs to develop a large-scale High-alpine Biodiversity
Monitoring Program to describe how the current climate change is shaping the
distribution of high altitude specialists, is highlighted.