pwc-archive/papers-with-abstracts huggingface dataset:
Absentee and Economic Impact of Low-Level Fine Particulate Matter and Ozone Exposure in K-12 Students
Date: 2020-07-16 00:00:00

High air pollution levels are associated with school absences. However, low
level pollution impact on individual school absences are under-studied. We
modelled PM2.5 and ozone concentrations at 36 schools from July 2015 to June
2018 using data from a dense, research grade regulatory sensor network. We
determined exposures and daily absences at each school. We used generalized
estimating equations model to retrospectively estimate rate ratios for
association between outdoor pollutant concentrations and school absences. We
estimated lost school revenue, productivity, and family economic burden. PM2.5
and ozone concentrations and absence rates vary across the School District.
Pollution exposure were associated with as high a rate ratio of 1.02 absences
per ug/m$^3$ and 1.01 per ppb increase for PM2.5 and ozone, respectively.
Significantly, even PM2.5 and ozone exposure below regulatory standards (<12.1
ug/m$^3$ and <55 ppb) was associated with positive rate ratios of absences:
1.04 per ug/m$^3$ and 1.01 per ppb increase, respectively. Granular local
measurements enabled demonstration of air pollution impacts that varied between
schools undetectable with averaged pollution levels. Reducing pollution by 50%
would save $452,000 per year districtwide. Pollution reduction benefits would
be greatest in schools located in socioeconomically disadvantaged areas.
Exposures to air pollution, even at low levels, are associated with increased
school absences. Heterogeneity in exposure, disproportionately affecting
socioeconomically disadvantaged schools, points to the need for fine resolution
exposure estimation. The economic cost of absences associated with air
pollution is substantial even excluding indirect costs such as hospital visits
and medication. These findings may help inform decisions about recess during
severe pollution events and regulatory considerations for localized pollution
sources.
Convolution Bounds on Quantile Aggregation
Authors: Jose Blanchet, Henry Lam, Yang Liu, Ruodu Wang
Date: 2020-07-18 00:00:00
Tasks: Management

Quantile aggregation with dependence uncertainty has a long history in probability theory with wide applications in finance, risk management, statistics, and operations research. Using a recent result on inf-convolution of quantile-based risk measures, we establish new analytical bounds for quantile aggregation which we call convolution bounds. Convolution bounds both unify every analytical result available in quantile aggregation and enlighten our understanding of these methods. These bounds are the best available in general. Moreover, convolution bounds are easy to compute, and we show that they are sharp in many relevant cases. They also allow for interpretability on the extremal dependence structure. The results directly lead to bounds on the distribution of the sum of random variables with arbitrary dependence. We discuss relevant applications in risk management and economics.
Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
Authors: Yuwen Yang, Feifei Gao, Chengwen Xing, Jianping An, Ahmed Alkhateeb
Date: 2020-07-18 00:00:00

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulations results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data in various wireless communication scenarios.
Perpetual American options with asset-dependent discounting
Date: 2020-07-18 00:00:00

In this paper we consider the following optimal stopping problem $$V^{\omega}_{\rm A}(s) = \sup_{\tau\in\mathcal{T}} \mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)],$$ where the process $S_t$ is a jump-diffusion process, $\mathcal{T}$ is a family of stopping times while $g$ and $\omega$ are fixed payoff function and discount function, respectively. In a financial market context, if $g(s)=(K-s)^+$ or $g(s)=(s-K)^+$ and $\mathbb{E}$ is the expectation taken with respect to a martingale measure, $V^{\omega}_{\rm A}(s)$ describes the price of a perpetual American option with a discount rate depending on the value of the asset process $S_t$. If $\omega$ is a constant, the above problem produces the standard case of pricing perpetual American options. In the first part of this paper we find sufficient conditions for the convexity of the value function $V^{\omega}_{\rm A}(s)$. This allows us to determine the stopping region as a certain interval and hence we are able to identify the form of $V^{\omega}_{\rm A}(s)$. We also prove a put-call symmetry for American options with asset-dependent discounting. In the case when $S_t$ is a geometric L\'evy process we give exact expressions using the so-called omega scale functions introduced in Li and Palmowski (2018). We prove that the analysed value function satisfies the HJB equation and we give sufficient conditions for the smooth fit property as well. Finally, we present a few examples for which we obtain the analytical form of the value function $V^{\omega}_{\rm A}(s)$.
3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks
Date: 2020-07-18 00:00:00

Computational Cannula Microscopy (CCM) is a high-resolution widefield
fluorescence imaging approach deep inside tissue, which is minimally invasive.
Rather than using conventional lenses, a surgical cannula acts as a lightpipe
for both excitation and fluorescence emission, where computational methods are
used for image visualization. Here, we enhance CCM with artificial neural
networks to enable 3D imaging of cultured neurons and fluorescent beads, the
latter inside a volumetric phantom. We experimentally demonstrate transverse
resolution of ~6um, field of view ~200um and axial sectioning of ~50um for
depths down to ~700um, all achieved with computation time of ~3ms/frame on a
laptop computer.
Optimal Consensus for Uncertain High-order Multi-agent Systems by Output Feedback
Authors: Yutao Tang, Kui Zhu
Date: 2020-07-18 00:00:00

The distributed optimal output consensus problem for high-order multi-agent systems has been studied recently. In this paper, we further focus on the same problem for high-order multi-agent systems subject to parametric uncertainties and aim at distributed robust controllers by measurement output feedback. We first develop a dynamic compensator to estimate the expected optimal consensus point and convert the problem into several decentralized robust tracking problems. Then, by combining the integral control technique and dirty derivative observer technique, we constructively propose a distributed output feedback integral controller to solve this problem under a mild graph connectivity condition.
Achieving Optimal Output Consensus for Discrete-time Linear Multi-agent Systems with Disturbances Rejection
Date: 2020-07-18 00:00:00

In this paper, an optimal output consensus problem is studied for
discrete-time linear multi-agent systems subject to external disturbances. Each
agent is assigned with a local cost function which is known only to itself.
Distributed protocols are to be designed to guarantee an output consensus for
these high-order agents and meanwhile minimize the aggregate cost as the sum of
these local costs. To overcome the difficulties brought by high-order dynamics
and external disturbances, we develop an embedded design and constructively
present a distributed rule to solve this problem. The proposed control includes
three terms: an optimal signal generator under a directed information graph, an
observer-based compensator to reject these disturbances, and a reference
tracking controller for these linear agents. It is shown to solve the
formulated problem with some mild assumptions. Numerical examples are also
provided to illustrate the effectiveness of our proposed distributed control
laws.
Leader-Driven Opinion Dynamics in Signed Social Networks With Asynchronous Trust/Distrust Level Evolution
Authors: Lei Shi, Yuhua Cheng, Jinliang Shao, Xiaofan Wang, Hanmin Sheng
Date: 2020-07-19 00:00:00

Trust and distrust are common in the opinion interactions among agents in social networks, and they are described by the edges with positive and negative weights in the signed digraph, respectively. It has been shown in social psychology that although the opinions of most agents (followers) tend to prevail, sometimes one agent (leader) with a firm stand and strong influence can impact or even overthrow the preferences of followers. This paper aims to analyze how the leader influences the formation of followers' opinions in signed social networks. In addition, this paper considers an asynchronous evolution mechanism of trust/distrust level based on opinion difference, in which the trust/distrust level between neighboring agents is portrayed as a nonlinear weight function of their opinion difference, and each agent interacts with the neighbors to update the trust/distrust level and opinion at the times determined by its own will. Based on the related properties of sub-stochastic and super-stochastic matrices, the inequality conditions about positive and negative weights to achieve opinion consensus and polarization are established. Some numerical simulations based on two well-known networks called the ``12 Angry Men" network and the Karate Club network are provided to verify the correctness of the theoretical results.
Only Time Will Tell: Credible Dynamic Signaling
Authors: Egor Starkov
Date: 2020-07-19 00:00:00

This paper characterizes informational outcomes in a model of dynamic signaling with vanishing commitment power. It shows that contrary to popular belief, informative equilibria with payoff-relevant signaling can exist without requiring unreasonable off-path beliefs. The paper provides a sharp characterization of possible separating equilibria: all signaling must take place through attrition, when the weakest type mixes between revealing own type and pooling with the stronger types. The framework explored in the paper is general, imposing only minimal assumptions on payoff monotonicity and single-crossing. Applications to bargaining, monopoly price signaling, and labor market signaling are developed to demonstrate the results in specific contexts.
Large Intelligent Surface Assisted Non-Orthogonal Multiple Access: Performance Analysis
Authors: Lina Bariah, Sami Muhaidat, Paschalis C. Sofotasios, Faissal El Bouanani, Octavia A. Dobre, Walaa Hamouda
Date: 2020-07-19 00:00:00
Tasks: Diversity

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted non-orthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and $L$ NOMA users is assisted by an LIS comprising $M$ reflective elements. First, we derive the probability density function of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for $M = 1$ and large $M$ values ($M > 10$) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for the PEP in high signal-to-noise ratio regime, the achievable diversity order, and a tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.
From individual-based epidemic models to McKendrick-von Foerster PDEs: A guide to modeling and inferring COVID-19 dynamics
Authors: Félix Foutel-Rodier, François Blanquart, Philibert Courau, Peter Czuppon, Jean-Jil Duchamps, Jasmine Gamblin, Élise Kerdoncuff, Rob Kulathinal, Léo Régnier, Laura Vuduc, Amaury Lambert, Emmanuel Schertzer
Date: 2020-07-19 00:00:00

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual's infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type $i$ is simply obtained by integrating the probability of being in state $i$ at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.
A Parametrized Nonlinear Predictive Control Strategy for Relaxing COVID-19 Social Distancing Measures in Brazil
Date: 2020-07-19 00:00:00
Tasks: Model Predictive Control

In this paper, we formulate a Nonlinear Model Predictive Control (NMPC) to
plan appropriate social distancing measures (and relaxations) in order to
mitigate the COVID-19 pandemic effects, considering the contagion development
in Brazil. The NMPC strategy is designed upon an adapted data-driven
Susceptible-Infected-Recovered-Deceased (SIRD) contagion model, which takes
into account the effects of social distancing. Furthermore, the adapted SIRD
model includes time-varying auto-regressive contagion parameters, which
dynamically converge according to the stage of the pandemic. This new model is
identified through a three-layered procedures, with analytical regressions,
Least-Squares optimization runs and auto-regressive model fits. The data-driven
model is validated and shown to adequately describe the contagion curves over
large forecast horizons. In this model, control input is defined as finitely
parametrized values for social distancing guidelines, which directly affect the
transmission and infection rates of the SARS-CoV-2 virus. The NMPC strategy
generates piece-wise constant quarantine guidelines which can be
relaxed/strengthen as each week passes. The implementation of the method is
pursued through a search mechanism, since the control is finitely parametrized
and, thus, there exist a finite number of possible control sequences.
Simulation essays are shown to illustrate the results obtained with the
proposed closed-loop NMPC strategy, which is able to mitigate the number of
infections and progressively loosen social distancing measures. With respect to
an "open-loop"/no control condition, the number of deaths still could be
reduced in up to 30 %. The forecast preview an infection peak to September 2nd,
2020, which could lead to over 1.5 million deaths if no coordinate health
policy is enacted. The framework serves as guidelines for possible public
health policies in Brazil.
A framework for studying behavioral evolution by reconstructing ancestral repertoires
Date: 2020-07-19 00:00:00

Although extensive behavioral changes often exist between closely related
animal species, our understanding of the genetic basis underlying the evolution
of behavior has remained limited. Here, we propose a new framework to study
behavioral evolution by computational estimation of ancestral behavioral
repertoires. We measured the behaviors of individuals from six species of fruit
flies using unsupervised techniques and identified suites of stereotyped
movements exhibited by each species. We then fit a Generalized Linear Mixed
Model to estimate the suites of behaviors exhibited by ancestral species, as
well as the intra- and inter-species behavioral covariances. We found that much
of intraspecific behavioral variation is explained by differences between
individuals in the status of their behavioral hidden states, what might be
called their "mood." Lastly, we propose a method to identify groups of
behaviors that appear to have evolved together, illustrating how sets of
behaviors, rather than individual behaviors, likely evolved. Our approach
provides a new framework for identifying co-evolving behaviors and may provide
new opportunities to study the genetic basis of behavioral evolution.
A Maximum Theorem for Incomplete Preferences
Authors: Leandro Gorno, Alessandro Rivello
Date: 2020-07-19 00:00:00

We extend Berge's Maximum Theorem to allow for incomplete preferences. We first provide a simple version of the Maximum Theorem for convex feasible sets and a fixed preference. Then, we show that if, in addition to the traditional continuity assumptions, a new continuity property for the domains of comparability holds, the limits of maximal elements along a sequence of decision problems are maximal elements in the limit problem. While this new continuity property for the domains of comparability is not generally necessary for optimality to be preserved by limits, we provide conditions under which it is necessary and sufficient.
Improving the Estimation of the COVID-19 Effective Reproduction Number using Nowcasting
Authors: Joaquín Salas
Date: 2020-07-19 00:00:00

As the interactions between people increases, the impending menace of COVID-19 outbreaks materialize, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to use as a reference. The basic reproduction number of confirmed positives, $R_t$, fulfill such a role. This document proposes a data-driven approach to nowcast $R_t$ based on previous observations' statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method's strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report's count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators.
Wireless Performance Evaluation of Building Layouts: Closed-Form Computation of Figures of Merit
Authors: Jiliang Zhang, Andrés Alayón Glazunov, Jie Zhang
Date: 2020-07-20 00:00:00
Tasks: Form

This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness in the building-design stage. The main goal is to devise construction practices that provide for a good performance of wireless networks deployed in buildings. In this paper, the interference gain (IG) and power gain (PG) are defined as two figures of merit (FoM) of the wireless performance of buildings. The FoMs bridge the gap between building design and wireless communications industries. An approach to derive exact closed-form equations for these FoMs is proposed for the first time. The derived analytic expressions facilitate straightforward and more computationally efficient numerical evaluation of the proposed FoMs as compared to Monte Carlo simulations for well-known indoor propagation models. It is shown that the derived closed-form expression can be readily employed to evaluate the impact of building properties, such as the sizes and the aspect ratios (ARs) of rooms, on the wireless performance. The proposed approach sheds light to architects on evaluation and design of wireless-friendly building layouts.
Permutation-based tests for discontinuities in event studies
Authors: Federico A. Bugni, Jia Li, Qiyuan Li
Date: 2020-07-20 00:00:00
Tasks: Time Series, Time Series Analysis

We propose using a permutation test to detect discontinuities in an underlying economic model at a known cutoff point. Relative to the existing literature, we show that this test is well suited for event studies based on time-series data. The test statistic measures the distance between the empirical distribution functions of observed data in two local subsamples on the two sides of the cutoff. Critical values are computed via a standard permutation algorithm. Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing the sizes of the local subsamples to be either be fixed or grow to infinity. In the latter case, we also establish that the permutation test is consistent. We demonstrate that our high-level condition can be verified in a broad range of problems in the infill asymptotic time-series setting, which justifies using the permutation test to detect jumps in economic variables such as volatility, trading activity, and liquidity. These potential applications are illustrated in an empirical case study for selected FOMC announcements during the ongoing COVID-19 pandemic.
Joint Transmit Power and Placement Optimization for URLLC-enabled UAV Relay Systems
Authors: Hong Ren, Cunhua Pan, Kezhi Wang, Wei Xu, Maged Elkashlan, Arumugam Nallanathan
Date: 2020-07-20 00:00:00

This letter considers an unmanned aerial vehicle (UAV)-enabled relay communication system for delivering latency-critical messages with ultra-high reliability, where the relay is operating under amplifier-and-forward (AF) mode. We aim to jointly optimize the UAV location and power to minimize decoding error probability while guaranteeing the latency constraints. Both the free-space channel model and three-dimensional (3-D) channel model are considered. For the first model, we propose a low-complexity iterative algorithm to solve the problem, while globally optimal solution is derived for the case when the signal-to-noise ratio (SNR) is extremely high. For the second model, we also propose a low-complexity iterative algorithm to solve the problem. Simulation results confirm the performance advantages of our proposed algorithms.