pwc-archive/papers-with-abstracts huggingface dataset:
Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach
Authors: João Victor Galvão da Mata, Anders Hansson, Martin S. Andersen
Date: 2023-11-01 00:00:00
Tasks: global-optimization

This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.
The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks
Authors: Shuai Feng, Maopeng Ran, Baoyong Zhang, Lihua Xie, Shengyuan Xu
Date: 2023-11-01 00:00:00
Tasks: Quantization

In this paper, we investigate tracking control of heterogeneous multi-agent systems under Denial-of-Service (DoS) attacks and state quantization. Dynamic quantized mechanisms are designed for inter-follower communication and leader-follower communication. Zooming-in and out factors, and data rates of both mechanisms for preventing quantizer saturation are provided. Our results show that by tuning the inter-follower quantized controller, one cannot improve the resilience beyond a level determined by the data rate of leader-follower quantized communication, i.e., the ceiling effect. Otherwise, overflow of followers' state quantizer can occur. On the other hand, if one selects a "large" data rate for leader-follower quantized communication, then the inter-follower quantized communication determines the resilience, and further increasing the data rate for leader-follower quantized communication cannot improve the resilience, i.e., the bottleneck effect. Simulation examples are provided to justify the results of our paper.
Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios
Authors: Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos
Date: 2023-10-31 00:00:00
Tasks: regression, Trajectory Planning, Uncertainty Quantification

Addressing safe and efficient interaction between connected and automated vehicles (CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable attention. In this paper, we develop a framework for stochastic time-optimal trajectory planning for coordinating multiple CAVs in mixed-traffic merging scenarios. We present a data-driven model, combining Newell's car-following model with Bayesian linear regression, for efficiently learning the driving behavior of human drivers online. Using the prediction model and uncertainty quantification, a stochastic time-optimal control problem is formulated to find robust trajectories for CAVs. We also integrate a replanning mechanism that determines when deriving new trajectories for CAVs is needed based on the accuracy of the Bayesian linear regression predictions. Finally, we demonstrate the performance of our proposed framework using a realistic simulation environment.
Harmonic content analysis of a soft starting variable frequency motor drive based on FPGA
Authors: Yogesh Sapkota, Suman Devkota, Vamsi Borra, Pedro Cortes, Frank Li, Srikanth Itapu
Date: 2023-10-28 00:00:00

As the demands for electric vehicles, electric aircrafts, unmanned aircraft systems, and other motor-driven systems increase, high-performance motor drives employing variable frequency control with higher efficiency and reliability are becoming increasingly important parts of the ever-changing technological landscape. This study proposes a Field Programmable Gate Array (FPGA)-based variable frequency soft-starting motor drive for a three-phase induction motor. The inverter output voltage and the load currents are analyzed for the harmonic contents using MATLAB. In the experimental realization, a four-pole squirrel cage delta-connected induction motor is utilized with a switching frequency of 4 kHz. The current and voltage characteristics of the induction motor are studied under different operating conditions to study harmonic contents and the effect of changing soft-start duration. The findings demonstrate a low-cost, flexible control of the induction motor with improved harmonic performance.
Multiple possible patterns can emerge from virus-immune coevolution
Authors: David A. Kessler, Herbert Levine
Date: 2023-11-01 00:00:00

The adaptive immune system engages in an arms race with evolving viruses, trying to generate new responses to viral strains that continually move away from the set of variants that have already elicited a functional immune response. In previous work, it has been argued that this dynamical process can lead to a propagating pulse of ever-changing viral population and concomitant immune response. Here, we introduce a new stochastic model of viral-host coevolution and a deterministic approximation thereof with which to study these dynamics. We show that there is indeed a possible pulse solution, but for a large host population size, the pulse becomes unstable to the generation of new infections in its wake, leading to an extended endemic infection pattern. This time-dependent endemic pattern eventually reaches a fluctuating steady-state when the infected population size reaches a large enough fraction of the total number of hosts.
The physical origin of aneurysm growth, dissection, and rupture
Authors: Tom Y. Zhao, Jin-Tae Kim, Min Cho, Akhil Narang, John A. Rogers, Neelesh A. Patankar
Date: 2023-11-01 00:00:00

Rupture of aortic aneurysms is by far the most fatal heart disease, with a mortality rate exceeding 80%. There are no reliable clinical protocols to predict growth, dissection, and rupture because the fundamental physics driving aneurysm progression is unknown. Here, via in-vitro experiments, we show that a blood-wall, fluttering instability manifests in synthetic arteries under pulsatile forcing. We establish a phase space to prove that the transition from stable flow to unstable aortic flutter is accurately predicted by a flutter instability parameter derived from first principles. Time resolved strain maps of the evolving system reveal the dynamical characteristics of aortic flutter that drive aneurysm progression. We show that low level instability can trigger permanent aortic growth, even in the absence of material remodeling. Sufficiently large flutter beyond a secondary threshold localizes strain in the walls to the length scale clinically observed in aortic dissection. Lastly, significant physical flutter beyond a tertiary threshold can ultimately induce aneurysm rupture via failure modes reported from necropsy. Resolving the fundamental physics of aneurysm progression directly leads to clinical protocols that forecast growth as well as intercept dissection and rupture by pinpointing their physical origin.
Limits on the accuracy of contact inhibition of locomotion
Authors: Wei Wang, Brian A. Camley
Date: 2023-10-31 00:00:00

Cells that collide with each other repolarize away from contact, in a process called contact inhibition of locomotion (CIL), which is necessary for correct development of the embryo. CIL can occur even when cells make a micron-scale contact with a neighbor - much smaller than their size. How precisely can a cell sense cell-cell contact and repolarize in the correct direction? What factors control whether a cell recognizes it has contacted a neighbor? We propose a theoretical model for the limits of CIL where cells recognize the presence of another cell by binding the protein ephrin with the Eph receptor. This recognition is made difficult by the presence of interfering ligands that bind nonspecifically. Both theoretical predictions and simulation results show that it becomes more difficult to sense cell-cell contact when it is difficult to distinguish ephrin from the interfering ligands, or when there are more interfering ligands, or when the contact width decreases. However, the error of estimating contact position remains almost constant when the contact width changes. This happens because the cell gains spatial information largely from the boundaries of cell-cell contact. We study using statistical decision theory the likelihood of a false positive CIL event in the absence of cell-cell contact, and the likelihood of a false negative where CIL does not occur when another cell is present. Our results suggest that the cell is more likely to make incorrect decisions when the contact width is very small or so large that it nears the cell's perimeter. However, in general, we find that cells have the ability to make reasonably reliable CIL decisions even for very narrow (micron-scale) contacts, even if the concentration of interfering ligands is ten times that of the correct ligands.
Role of Structural and Conformational Diversity for Machine Learning Potentials
Authors: Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
Date: 2023-10-30 00:00:00
Tasks: Diversity

In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts.
A Two-Step Framework for Multi-Material Decomposition of Dual Energy Computed Tomography from Projection Domain
Authors: Di Xu, Qihui Lyu, Dan Ruan, Ke Sheng
Date: 2023-10-31 00:00:00
Tasks: Benchmarking, Diagnostic, Domain Adaptation, GPU, SSIM

Dual-energy computed tomography (DECT) utilizes separate X-ray energy spectra to improve multi-material decomposition (MMD) for various diagnostic applications. However accurate decomposing more than two types of material remains challenging using conventional methods. Deep learning (DL) methods have shown promise to improve the MMD performance, but typical approaches of conducing DL-MMD in the image domain fail to fully utilize projection information or under iterative setup are computationally inefficient in both training and prediction. In this work, we present a clinical-applicable MMD (>2) framework rFast-MMDNet, operating with raw projection data in non-recursive setup, for breast tissue differentiation. rFast-MMDNet is a two-stage algorithm, including stage-one SinoNet to perform dual energy projection decomposition on tissue sinograms and stage-two FBP-DenoiseNet to perform domain adaptation and image post-processing. rFast-MMDNet was tested on a 2022 DL-Spectral-Challenge breast phantom dataset. The two stages of rFast-MMDNet were evaluated separately and then compared with four noniterative reference methods including a direct inversion method (AA-MMD), an image domain DL method (ID-UNet), AA-MMD/ID-UNet + DenoiseNet and a sinogram domain DL method (Triple-CBCT). Our results show that models trained from information stored in DE transmission domain can yield high-fidelity decomposition of the adipose, calcification, and fibroglandular materials with averaged RMSE, MAE, negative PSNR, and SSIM of 0.004+/-~0, 0.001+/-~0, -45.027+/-~0.542, and 0.002+/-~0 benchmarking to the ground truth, respectively. Training of entire rFast-MMDNet on a 4xRTX A6000 GPU cluster took a day with inference time <1s. All DL methods generally led to more accurate MMD than AA-MMD. rFast-MMDNet outperformed Triple-CBCT, but both are superior to the image-domain based methods.
SIRS epidemics with individual heterogeneity of immunity waning
Authors: Mohamed El Khalifi, Tom Britton
Date: 2023-11-01 00:00:00

We analyse an extended SIRS epidemic model in which immunity at the individual level wanes gradually at exponential rate, but where the waning rate may differ between individuals, for instance as an effect of differences in immune systems. The model also includes vaccination schemes aimed to reach and maintain herd immunity. We consider both the informed situation where the individual waning parameters are known, thus allowing selection of vaccinees being based on both time since last vaccination as well as on the individual waning rate, and the more likely uninformed situation where individual waning parameters are unobserved, thus only allowing vaccination schemes to depend on time since last vaccination. The optimal vaccination policies for both the informed and uniformed heterogeneous situation are derived and compared with the homogeneous waning model (meaning all individuals have the same immunity waning rate), as well as to the classic SIRS model where immunity at the individual level drops from complete immunity to complete susceptibility in one leap. It is shown that the classic SIRS model requires least vaccines, followed by the SIRS with homogeneous gradual waning, followed by the informed situation for the model with heterogeneous gradual waning. The situation requiring most vaccines for herd immunity is the most likely scenario, that immunity wanes gradually with unobserved individual heterogeneity. For parameter values chosen to mimic COVID-19 and assuming perfect initial immunity and cumulative immunity of 12 months, the classic homogeneous SIRS epidemic suggests that vaccinating individuals every 15 months is sufficient to reach and maintain herd immunity, whereas the uninformed case for exponential waning with rate heterogeneity corresponding to a coefficient of variation being 0.5, requires that individuals instead need to be vaccinated every 4.4 months.
The learning effects of subsidies to bundled goods: a semiparametric approach
Authors: Luis Alvarez, Ciro Biderman
Date: 2023-11-02 00:00:00

Can temporary subsidies to bundles induce long-run changes in demand due to learning about the quality of one of the constituent goods? This paper provides theoretical support and empirical evidence on this mechanism. Theoretically, we introduce a model where an agent learns about the quality of an innovation through repeated consumption. We then assess the predictions of our theory in a randomised experiment in a ridesharing platform. The experiment subsidised car trips integrating with a train or metro station, which we interpret as a bundle. Given the heavy-tailed nature of our data, we propose a semiparametric specification for treatment effects that enables the construction of more efficient estimators. We then introduce an efficient estimator for our specification by relying on L-moments. Our results indicate that a ten-weekday 50\% discount on integrated trips leads to a large contemporaneous increase in the demand for integration, and, consistent with our model, persistent changes in the mean and dispersion of nonintegrated app rides. These effects last for over four months. A calibration of our theoretical model suggests that around 40\% of the contemporaneous increase in integrated rides may be attributable to increased incentives to learning. Our results have nontrivial policy implications for the design of public transit systems.
On Gaussian Process Priors in Conditional Moment Restriction Models
Authors: Sid Kankanala
Date: 2023-11-01 00:00:00
Tasks: Uncertainty Quantification

This paper studies quasi Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction. We derive contraction rates for a class of Gaussian process priors. Furthermore, we provide conditions under which a Bernstein von Mises theorem holds for the quasi-posterior distribution. As a consequence, we show that optimally weighted quasi-Bayes credible sets have exact asymptotic frequentist coverage.
Robustify and Tighten the Lee Bounds: A Sample Selection Model under Stochastic Monotonicity and Symmetry Assumptions
Authors: Yuta Okamoto
Date: 2023-11-01 00:00:00

In the presence of sample selection, Lee's (2009) nonparametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, whose empirical validity is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity assumption alongside a nonparametric distributional shape constraint. The former enhances the robustness of the Lee bounds with respect to monotonicity, while the latter helps tighten these bounds. The obtained bounds do not rely on the exclusion restriction and can be root-$n$ consistently estimable, making them practically viable. The potential usefulness of the proposed methods is illustrated by their application on experimental data from the after-school instruction programme studied by Muralidharan, Singh, and Ganimian (2019).
Semiparametric Discrete Choice Models for Bundles
Authors: Fu Ouyang, Thomas Tao Yang
Date: 2023-10-31 00:00:00
Tasks: Discrete Choice Models

We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identification strategy. We establish its complete asymptotic properties and prove the validity of the nonparametric bootstrap for inference. We then introduce a new multi-index least absolute deviations (LAD) estimator as an alternative, of which the main advantage is its capacity to estimate preference parameters on both alternative- and agent-specific regressors. Both methods can account for arbitrary correlation in disturbances across choices, with the former also allowing for interpersonal heteroskedasticity. We also demonstrate that the identification strategy underlying these procedures can be extended naturally to panel data settings, producing an analogous localized maximum score estimator and a LAD estimator for estimating bundle choice models with fixed effects. We derive the limiting distribution of the former and verify the validity of the numerical bootstrap as an inference tool. All our proposed methods can be applied to general multi-index models. Monte Carlo experiments show that they perform well in finite samples.
A connected and automated vehicle readiness framework to support road authorities for C-ITS services
Authors: Bahman Madadi, Ary P. Silvano, Kevin McPherson, John McCarthy, Risto Öörni, Gonçalo Homem de Almeida Correiaa
Date: 2023-11-02 00:00:00

Connected and Automated Vehicles (CAVs) can have a profound influence on transport systems. However, most levels of automation and connectivity require support from the road infrastructure. Additional support such as Cooperative Intelligent Transport Systems (C-ITS) services can facilitate safe and efficient traffic, and alleviate the environmental impacts of road surface vehicles. However, due to the rapidly evolving technology, C-ITS service deployment requirements are not always clear. Furthermore, the costs and benefits of infrastructure investments are subject to tremendous uncertainty. This study articulates the requirements using a structured approach to propose a CAV-Readiness Framework (CRF). The main purpose of the CRF is allowing road authorities to assess their physical and digital infrastructure readiness, define requirements for C-ITS services, and identify future development paths to reach higher levels of readiness to support CAVs by enabling C-ITS services. The CRF is intended to guide and support road authorities' investment decisions on infrastructure.
How Does China's Household Portfolio Selection Vary with Financial Inclusion?
Authors: Yong Bian, Xiqian Wang, Qin Zhang
Date: 2023-11-02 00:00:00

Portfolio underdiversification is one of the most costly losses accumulated over a household's life cycle. We provide new evidence on the impact of financial inclusion services on households' portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households.
What is a Labor Market? Classifying Workers and Jobs Using Network Theory
Authors: Jamie Fogel, Bernardo Modenesi
Date: 2023-11-01 00:00:00

This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We microfound this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. Our classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on our classifications more accurately predicts wage changes in response to the 2016 Olympics than a model based on occupations and sectors. Additionally, for a large simulated shock to demand for workers, we show that reduced form estimates of the effects of labor market shock exposure on workers' earnings are nearly 4 times larger when workers and jobs are classified using our classifications as opposed to occupations and sectors.
From Doubt to Devotion: Trials and Learning-Based Pricing
Authors: Tan Gan, Nicholas Wu
Date: 2023-11-01 00:00:00

An informed seller designs a dynamic mechanism to sell an experience good. The seller has partial information about the product match, which affects the buyer's private consumption experience. We characterize equilibrium mechanisms of this dynamic informed principal problem. The belief gap between the informed seller and the uninformed buyer, coupled with the buyer's learning, gives rise to mechanisms that provide the skeptical buyer with limited access to the product and an option to upgrade if the buyer is swayed by a good experience. Depending on the seller's screening technology, this takes the form of free/discounted trials or tiered pricing, which are prevalent in digital markets. In contrast to static environments, having consumer data can reduce sellers' revenue in equilibrium, as they fine-tune the dynamic design with their data forecasting the buyer's learning process.