pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous Suspension Control and Energy Harvesting through Novel Design and Control of a New Nonlinear Energy Harvesting Shock Absorber
Authors: Mohammad R. Hajidavalloo, Joel Cosner, Zhaojian Li, Wei-Che Tai, Ziyou Song
Date: 2021-06-19 00:00:00
Tasks: Model Predictive Control

Simultaneous vibration control and energy harvesting of vehicle suspensions have attracted significant research attention over the past decades. However, existing energy harvesting shock absorbers (EHSAs) are mainly designed based on the principle of linear resonance, thereby compromising suspension performance for high-efficiency energy harvesting and being only responsive to narrow bandwidth vibrations. In this paper, we propose a new EHSA design -- inerter pendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber. We show that this design simultaneously improves ride comfort and energy harvesting efficiency by exploiting the nonlinear effects of pendulum inertia. To further improve the performance, we develop a novel stochastic linearization model predictive control (SL-MPC) approach in which we employ stochastic linearization to approximate the nonlinear dynamics of EHSA that has superior accuracy compared to standard linearization. In particular, we develop a new stochastic linearization method with guaranteed stabilizability, which is a prerequisite for control designs. This leads to an MPC problem that is much more computationally efficient than the nonlinear MPC counterpart with no major performance degradation. Extensive simulations are performed to show the superiority of the proposed new nonlinear EHSA and to demonstrate the efficacy of the proposed SL-MPC.
Cooperative Evasion by Translating Targets with Variable Speeds
Authors: Shivam Bajaj, Eloy Garcia, Shaunak D. Bopardikar
Date: 2021-06-19 00:00:00

We consider a problem of cooperative evasion between a single pursuer and multiple evaders in which the evaders are constrained to move in the positive Y direction. The evaders are slower than the vehicle and can choose their speeds from a bounded interval. The pursuer aims to intercept all evaders in a given sequence by executing a Manhattan pursuit strategy of moving parallel to the X axis, followed by moving parallel to the Y axis. The aim of the evaders is to cooperatively pick their individual speeds so that the total time to intercept all evaders is maximized. We first obtain conditions under which evaders should cooperate in order to maximize the total time to intercept as opposed to each moving greedily to optimize its own intercept time. Then, we propose and analyze an algorithm that assigns evasive strategies to the evaders in two iterations as opposed to performing an exponential search over the choice of evader speeds. We also characterize a fundamental limit on the total time taken by the pursuer to capture all evaders when the number of evaders is large. Finally, we provide numerical comparisons against random sampling heuristics.
A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks
Authors: Tianci Yang, Carlos Murguia, Dragan Nešić, Chen Lv
Date: 2021-06-19 00:00:00

Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{\infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach.
On the Impact of the Capacity Drop Phenomenon for Freeway Traffic Flow Control
Authors: Michael Enqi Cao, Gustav Nilsson, Samuel Coogan
Date: 2021-06-18 00:00:00

Capacity drop is an empirically observed phenomenon in vehicular traffic flow on freeways whereby, after a critical density is reached, a state of congestion sets in, but the freeway does not become decongested again until the density drops well below the critical density. This introduces a hysteresis effect so that it is easier to enter the congested state than to leave it. However, many existing first-order models of traffic flow, particularly those used for control design, ignore capacity drop, leading to suboptimal controllers. In this paper, we consider a cell transmission model of traffic flow that incorporates capacity drop to study the problem of optimal freeway ramp metering. We show that, if capacity drop is ignored in the control design, then the resulting controller, obtained via a convex program, may be significantly suboptimal. We then propose an alternative model predictive controller that accounts for capacity drop via a mixed integer linear program and show that, for sufficiently large rollout horizon, this controller is optimal. We also compare these approaches to a heuristic hand-crafted controller that is viewed as a modification of an integral feedback controller to account for capacity drop. This heuristic controller outperforms the controller that ignores capacity drop but underperforms compared to the proposed alternative model predictive controller. These results suggest that it is generally important to include capacity drop in the controller design process, and we demonstrate this insight on several case studies.
3D Printed Metallic Dual-Polarized Vivaldi Arrays on Square and Triangular Lattices
Authors: Carl Pfeiffer, Jeffrey Massman, Thomas Steffen
Date: 2021-06-10 00:00:00

We report the first Vivaldi arrays monolithically fabricated exclusively using commercial, low-cost, 3D metal printing (direct metal laser sintering). Furthermore, we developed one of the first dual-polarized Vivaldi arrays on a triangular lattice, and compare it to a square lattice array. The triangular lattice is attractive because it has a 15.5% larger cell size compared to the square lattice and can be more naturally truncated into a wide range of aperture shapes such as a rectangle, hexagon, or triangle. Both arrays operate at 3-20 GHz and scan angles out to 60 degree from normal. The fabrication process is significantly simplified compared to previously published Vivaldi arrays since the antenna is ready for use directly after the standard printing process is complete. This rapid manufacturing is further expedited by printing the 'Sub-Miniature Push-on, Micro' (SMPM) connectors directly onto the radiating elements, which simplifies assembly and reduces cost compared to utilizing discrete RF connectors. The arrays have a modular design that allow for combining multiple sub-arrays together for arbitrarily increasing the aperture size. Simulations and measurement show that our arrays have similar performance as previously published Vivaldi arrays, but with simpler fabrication.
An Automated Global Method for Extraction of Distance Distributions from Electron Spin Resonance Pulsed Dipolar Signals
Authors: Aritro Sinha Roy
Date: 2021-06-18 00:00:00
Tasks: Protein Structure Prediction

Electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS) is used effectively in measuring nano-meter range distances for protein structure prediction. The current global approach in extracting the distance distribution from time domain PDS signal has multiple limitations. We present a parameter free global method, which is more efficacious and less sensitive to signal noise compared to the current method.
A mathematical perspective on edge-centric brain functional connectivity
Authors: Leonardo Novelli, Adeel Razi
Date: 2021-06-20 00:00:00
Tasks: Functional Connectivity, Time Series, Time Series Analysis

Edge time series are increasingly used in brain functional imaging to study the node functional connectivity (nFC) dynamics at the finest temporal resolution while avoiding sliding windows. Here, we lay the mathematical foundations for the edge-centric analysis of neuroimaging time series, explaining why a few high-amplitude cofluctuations drive the nFC across datasets. Our exposition also constitutes a critique of the existing edge-centric studies, showing that their main findings can be derived from the nFC under a static null hypothesis that disregards temporal correlations. Testing the analytic predictions on functional MRI data from the Human Connectome Project confirms that the nFC can explain most variation in the edge FC matrix, the edge communities, the large cofluctuations, and the corresponding spatial patterns. We encourage the use of dynamic measures in future research, which exploit the temporal structure of the edge time series and cannot be replicated by static null models.
A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams
Authors: Yifeng Zhao, Zicheng Liu, Pei Zhang, S. A. Galindo-Torres, Stan Z. Li
Date: 2021-06-17 00:00:00
Tasks: regression, Symbolic Regression

A better understanding of dispersion in natural streams requires knowledge of longitudinal dispersion coefficient(LDC). Various methods have been proposed for predictions of LDC. Those studies can be grouped into three types: analytical, statistical and ML-driven researches(Implicit and explicit). However, a comprehensive evaluation of them is still lacking. In this paper, we first present an in-depth analysis of those methods and find out their defects. This is carried out on an extensive database composed of 660 samples of hydraulic and channel properties worldwide. The reliability and representativeness of utilized data are enhanced through the deployment of the Subset Selection of Maximum Dissimilarity(SSMD) for testing set selection and the Inter Quartile Range(IQR) for removal of the outlier. The evaluation reveals the rank of those methods as: ML-driven method > the statistical method > the analytical method. Whereas implicit ML-driven methods are black-boxes in nature, explicit ML-driven methods have more potential in prediction of LDC. Besides, overfitting is a universal problem in existing models. Those models also suffer from a fixed parameter combination. To establish an interpretable model for LDC prediction with higher performance, we then design a novel symbolic regression method called evolutionary symbolic regression network(ESRN). It is a combination of genetic algorithms and neural networks. Strategies are introduced to avoid overfitting and explore more parameter combinations. Results show that the ESRN model has superiorities over other existing symbolic models in performance. The proposed model is suitable for practical engineering problems due to its advantage in low requirement of parameters (only w and U* are required). It can provide convincing solutions for situations where the field test cannot be carried out or limited field information can be obtained.
Effect of time-dependent infectiousness on epidemic dynamics
Authors: Nicholas Landry
Date: 2021-06-18 00:00:00

In contrast to the common assumption in epidemic models that the rate of infection between individuals is constant, in reality, an individual's viral load determines their infectiousness. We compare the average and individual reproductive numbers and epidemic dynamics for a model incorporating time-dependent infectiousness and a standard SIR model for both fully-mixed and category-mixed populations. We find that the reproductive number only depends on the total infectious exposure and the largest eigenvalue of the mixing matrix and that these two effects are independent of each other. When we compare our time-dependent mean-field model to the SIR model with equivalent rates, the epidemic peak is advanced and modifying the infection rate function has a strong effect on the time dynamics of the epidemic. We also observe behavior akin to a traveling wave as individuals transition through infectious states.
Identifying Mitigation Strategies for COVID-19 Superspreading on Flights using Models that Account for Passenger Movement
Authors: Sirish Namilae, Yuxuan Wu, Anuj Mubayi, Ashok Srinivasan, Matthew Scotch
Date: 2021-06-18 00:00:00

Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. We used available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. We show that inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. This suggests the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.
Witten-type topological field theory of self-organized criticality for stochastic neural networks
Authors: Jian Zhai, Chaojun Yu, You Zhai
Date: 2021-06-21 00:00:00
Tasks: Quantization

We study the Witten-type topological field theory(W-TFT) of self-organized criticality(SOC) for stochastic neural networks. The Parisi-Sourlas-Wu quantization of general stochastic differential equations (SDEs) for neural networks, the Becchi-Rouet-Stora-Tyutin(BRST)-symmetry of the diffusion system and the relation between spontaneous breaking and instantons connecting steady states of the SDEs, as well as the sufficient and necessary condition on pseudo-supersymmetric stochastic neural networks are obtained. Suppose neuronal avalanche is a mechanism of cortical information processing and storage \cite{Beggs}\cite{Plenz1}\cite{Plenz2} and the model of stochastic neural networks\cite{Dayan} is correct, as well as the SOC system can be looked upon as a W-TFT with spontaneously broken BRST symmetry. Then we should recover the neuronal avalanches and spontaneously broken BRST symmetry from the model of stochastic neural networks. We find that, provided the divergence of drift coefficients is small and non-constant, the model of stochastic neural networks is BRST symmetric. That is, if the SOC of brain neural networks system can be looked upon as a W-TFT with spontaneously broken BRST symmetry, then the general model of stochastic neural networks which be extensively used in neuroscience \cite{Dayan} is not enough to describe the SOC. On the other hand, using the Fokker-Planck equation, we show the sufficient condition on diffusion so that there exists a steady state probability distribution for the stochastic neural networks. Rhythms of the firing rates of the neuronal networks arise from the process, meanwhile some biological laws are conserved.
On Testing Equal Conditional Predictive Ability Under Measurement Error
Authors: Yannick Hoga, Timo Dimitriadis
Date: 2021-06-21 00:00:00

Loss functions are widely used to compare several competing forecasts. However, forecast comparisons are often based on mismeasured proxy variables for the true target. We introduce the concept of exact robustness to measurement error for loss functions and fully characterize this class of loss functions as the Bregman class. For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables and, thus, inference on conditional predictive ability can be carried out as usual. Moreover, we show that more precise proxies give predictive ability tests higher power in discriminating between competing forecasts. Simulations illustrate the different behavior of exactly robust and non-robust loss functions. An empirical application to US GDP growth rates demonstrates that it is easier to discriminate between forecasts issued at different horizons if a better proxy for GDP growth is used.
On the Use of Two-Way Fixed Effects Models for Policy Evaluation During Pandemics
Authors: Germain Gauthier
Date: 2021-06-21 00:00:00

In the context of the Covid-19 pandemic, multiple studies rely on two-way fixed effects (FE) models to assess the impact of mitigation policies on health outcomes. Building on the SIRD model of disease transmission, I show that FE models tend to be misspecified for three reasons. First, despite misleading common trends in the pre-treatment period, the parallel trends assumption generally does not hold. Second, heterogeneity in infection rates and infected populations across regions cannot be accounted for by region-specific fixed effects, nor by conditioning on observable time-varying confounders. Third, epidemiological theory predicts heterogeneous treatment effects across regions and over time. Via simulations, I find that the bias resulting from model misspecification can be substantial, in magnitude and sometimes in sign. Overall, my results caution against the use of FE models for mitigation policy evaluation.
A Neural Frequency-Severity Model and Its Application to Insurance Claims
Authors: Dong-Young Lim
Date: 2021-06-20 00:00:00

This paper proposes a flexible and analytically tractable class of frequency and severity models for predicting insurance claims. The proposed model is able to capture nonlinear relationships in explanatory variables by characterizing the logarithmic mean functions of frequency and severity distributions as neural networks. Moreover, a potential dependence between the claim frequency and severity can be incorporated. In particular, the paper provides analytic formulas for mean and variance of the total claim cost, making our model ideal for many applications such as pricing insurance contracts and the pure premium. A simulation study demonstrates that our method successfully recovers nonlinear features of explanatory variables as well as the dependency between frequency and severity. Then, this paper uses a French auto insurance claim dataset to illustrate that the proposed model is superior to the existing methods in fitting and predicting the claim frequency, severity, and the total claim loss. Numerical results indicate that the proposed model helps in maintaining the competitiveness of an insurer by accurately predicting insurance claims and avoiding adverse selection.
Semiparametric inference for partially linear regressions with Box-Cox transformation
Authors: Daniel Becker, Alois Kneip, Valentin Patilea
Date: 2021-06-20 00:00:00
Tasks: Econometrics

In this paper, a semiparametric partially linear model in the spirit of Robinson (1988) with Box- Cox transformed dependent variable is studied. Transformation regression models are widely used in applied econometrics to avoid misspecification. In addition, a partially linear semiparametric model is an intermediate strategy that tries to balance advantages and disadvantages of a fully parametric model and nonparametric models. A combination of transformation and partially linear semiparametric model is, thus, a natural strategy. The model parameters are estimated by a semiparametric extension of the so called smooth minimum distance (SmoothMD) approach proposed by Lavergne and Patilea (2013). SmoothMD is suitable for models defined by conditional moment conditions and allows the variance of the error terms to depend on the covariates. In addition, here we allow for infinite-dimension nuisance parameters. The asymptotic behavior of the new SmoothMD estimator is studied under general conditions and new inference methods are proposed. A simulation experiment illustrates the performance of the methods for finite samples.
Framing and Social Information Nudges at Wikipedia
Authors: Maximilian Linek, Christian Traxler
Date: 2021-06-21 00:00:00
Tasks: Survey

We analyze a series of trials that randomly assigned Wikipedia users in Germany to different web banners soliciting donations. The trials varied framing or content of social information about how many other users are donating. Framing a given number of donors in a negative way increased donation rates. Variations in the communicated social information had no detectable effects. The findings are consistent with the results from a survey experiment. In line with donations being strategic substitutes, the survey documents that the negative framing lowers beliefs about others' donations. Varying the social information, in contrast, is ineffective in changing average beliefs.
Doctors and Nurses Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States
Authors: Emily Breza, Fatima Cody Stanford, Marcela Alsan, M. D. Ph. D., Burak Alsan, Abhijit Banerjee, Arun G. Chandrasekhar, Sarah Eichmeyer, Traci Glushko, Paul Goldsmith-Pinkham, Kelly Holland, Emily Hoppe, Mohit Karnani, Sarah Liegl, Tristan Loisel, Lucy Ogbu-Nwobodo, Benjamin A. Olken Carlos Torres, Pierre-Luc Vautrey, Erica Warner, Susan Wootton, Esther Duflo
Date: 2021-06-21 00:00:00

During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.