pwc-archive/papers-with-abstracts huggingface dataset:
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis
Authors: Ali Baheri, Mykel J. Kochenderfer
Date: 2023-05-10 00:00:00
Tasks: Efficient Exploration

Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessitates extensive testing and verification of control policies, typically conducted in simulation environments. High-fidelity simulators accurately model real-world dynamics but entail high computational costs, limiting their scalability for exhaustive testing. Conversely, low-fidelity simulators offer efficiency but may not capture the intricacies of high-fidelity simulators, potentially yielding false conclusions. We propose a joint falsification and fidelity optimization framework for safety validation of autonomous systems. Our mathematical formulation combines counterexample searches with simulator fidelity improvement, facilitating more efficient exploration of the critical environmental configurations challenging the control system. Our contributions encompass a set of theorems addressing counterexample sensitivity analysis, sample complexity, convergence, the interplay between the outer and inner optimization loops, and regret bound analysis. The proposed joint optimization approach enables a more targeted and efficient testing process, optimizes the use of available computational resources, and enhances confidence in autonomous system safety validation.
On receding-horizon approximation in time-varying optimal control
Authors: Jintao Sun, Michael Cantoni
Date: 2023-05-10 00:00:00

The closed-loop stability and infinite-horizon performance of receding-horizon approximations are studied for non-stationary linear-quadratic regulator (LQR) problems. The approach is based on a lifted reformulation of the optimal control problem, under assumed uniform controllability and observability, leading to a strict contraction property of the corresponding Riccati operator. Leveraging this contraction property, a stabilizing linear time-varying state-feedback approximation of the infinite-horizon optimal control policy is constructed to meet a performance-loss specification. Its synthesis involves only finite preview of the time-varying problem data at each time step, over a sufficiently long prediction horizon.
On Riccati contraction in time-varying linear-quadratic control
Authors: Jintao Sun, Michael Cantoni
Date: 2023-05-10 00:00:00

Contraction properties of the Riccati operator are studied within the context of non-stationary linear-quadratic optimal control. A lifting approach is used to obtain a bound on the rate of strict contraction, with respect to the Riemannian metric, across a sufficient number of iterations. This number of iterations is related to an assumed uniform controllability and observability property of the dynamics and stage-cost in the original formulation of the problem.
Structured Kalman Filter for Time Scale Generation in Atomic Clock Ensembles
Authors: Yuyue Yan, Takahiro Kawaguchi, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki
Date: 2023-05-10 00:00:00

In this article, we present a structured Kalman filter associated with the transformation matrix for observable Kalman canonical decomposition from conventional Kalman filter (CKF) in order to generate a more accurate time scale. The conventional Kalman filter is a special case of the proposed structured Kalman filter which yields the same predicted unobservable or observable states when some conditions are satisfied. We consider an optimization problem respective to the transformation matrix where the objective function is associated with not only the expected value of prediction error but also its variance. We reveal that such an objective function is a convex function and show some conditions under which CKF is nothing but the optimal algorithm if ideal computation is possible without computation error. A numerical example is presented to show the robustness of the proposed method in terms of the initial error covariance
New Formulations and Pricing Mechanisms for Stochastic Electricity Market Clearing Problem
Authors: Sakitha Ariyarathne, Harsha Gangammanavar
Date: 2023-05-10 00:00:00

We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guarantees of revenue adequacy, cost recovery, and price distortion in expectation. Motivated by these results, we develop alternative stochastic programming model formulations that differ in how we model the nonanticipativity requirement. We design new pricing mechanisms by developing suitable dual optimization problems for these models. We analyze these pricing mechanisms to assess their ability to provide revenue adequacy for the system operators, cost recovery for generators, and price distortion. Unlike the previous mechanisms that yield the desired properties in expectation, one of our pricing mechanisms extends the benefits of cost recovery and bounded price distortion under every scenario while retaining the long-run revenue adequacy for the system operator. We demonstrate these benefits through numerical experiments conducted on well-known test systems.
Constrained reaction wheel desaturation and attitude control of spacecraft with four reaction wheels
Authors: Miguel Castroviejo-Fernandez, Ilya Kolmanovsky
Date: 2023-05-10 00:00:00
Tasks: Model Predictive Control

The paper addresses a problem of constrained spacecraft attitude stabilization with simultaneous reaction wheel (RW) desaturation. The spacecraft has a reaction wheel array (RWA) consisting of four RWs in a pyramidal configuration. The developments exploit a spacecraft dynamics model with gravity gradient torques. The linearized dynamics are shown to be controllable at almost all RWA configurations. Configurations that result in the highest Degree of Controllability are elucidated. A strategy that combines an incremental reference governor and time-distributed model predictive control is proposed to perform constrained RW desaturation at low computational cost. Simulation results of successful RW desaturation maneuvers subject to spacecraft pointing constraints, RW zero-speed crossing avoidance and limits on control moments are reported.
Surrogate-based optimisation of process systems to recover resources from wastewater
Authors: Alex Durkin, Miao Guo
Date: 2023-05-09 00:00:00

Wastewater systems are transitioning towards integrative process systems to recover multiple resources whilst simultaneously satisfying regulations on final effluent quality. This work contributes to the literature by bringing a systems-thinking approach to resource recovery from wastewater, harnessing surrogate modelling and mathematical optimisation techniques to highlight holistic process systems. A surrogate-based process synthesis methodology was presented to harness high-fidelity data from black box process simulations, embedding first principles models, within a superstructure optimisation framework. Modelling tools were developed to facilitate tailored derivative-free optimisation solutions widely applicable to black box optimisation problems. The optimisation of a process system to recover energy and nutrients from a brewery wastewater reveals significant scope to reduce the environmental impacts of food and beverage production systems. Additionally, the application demonstrates the capabilities of the modelling methodology to highlight optimal processes to recover carbon, nitrogen, and phosphorous resources whilst also accounting for uncertainties inherent to wastewater systems.
The price elasticity of Gleevec in patients with Chronic Myeloid Leukemia enrolled in Medicare Part D: Evidence from a regression discontinuity design
Authors: Samantha E. Clark, Ruth Etzioni, Jerry Radich, Zachary Marcum, Anirban Basu
Date: 2023-05-10 00:00:00

Objective To assess the price elasticity of branded imatinib in chronic myeloid leukemia (CML) patients on Medicare Part D to determine if high out-of-pocket payments (OOP) are driving the substantial levels of non-adherence observed in this population. Data sources and study setting We use data from the TriNetX Diamond Network (TDN) United States database for the period from first availability in 2011 through the end of patent exclusivity following the introduction of generic imatinib in early 2016. Study design We implement a fuzzy regression discontinuity design to separately estimate the effect of Medicare Part D enrollment at age 65 on adherence and OOP in newly-diagnosed CML patients initiating branded imatinib. The corresponding price elasticity of demand (PED) is estimated and results are assessed across a variety of specifications and robustness checks. Data collection/extraction methods Data from eligible patients following the application of inclusion and exclusion criteria were analyzed. Principal findings Our analysis suggests that there is a significant increase in initial OOP of $232 (95% Confidence interval (CI): $102 to $362) for individuals that enrolled in Part D due to expanded eligibility at age 65. The relatively smaller and non-significant decrease in adherence of only 6 percentage points (95% CI: -0.21 to 0.08) led to a PED of -0.02 (95% CI: -0.056, 0.015). Conclusion This study provides evidence regarding the financial impact of coinsurance-based benefit designs on Medicare-age patients with CML initiating branded imatinib. Results indicate that factors besides high OOP are driving the substantial non-adherence observed in this population and add to the growing literature on PED for specialty drugs.
On the Time-Varying Structure of the Arbitrage Pricing Theory using the Japanese Sector Indices
Authors: Koichiro Moriya, Akihiko Noda
Date: 2023-05-10 00:00:00

This paper is the first study to examine the time instability of the APT in the Japanese stock market. In particular, we measure how changes in each risk factor affect the stock risk premiums to investigate the validity of the APT over time, applying the rolling window method to Fama and MacBeth's (1973) two-step regression and Kamstra and Shi's (2023) generalized GRS test. We summarize our empirical results as follows: (1) the changes in monetary policy by major central banks greatly affect the validity of the APT in Japan, and (2) the time-varying estimates of the risk premiums for each factor are also unstable over time, and they are affected by the business cycle and economic crises. Therefore, we conclude that the validity of the APT as an appropriate model to explain the Japanese sector index is not stable over time.
Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?
Authors: Jie Wei, Yonghui Zhang
Date: 2023-05-10 00:00:00

This paper studies the principal component (PC) method-based estimation of weak factor models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for the PC estimators of loadings, which comes from the approximately upper triangular (block) structure of the rotation matrix. It implies an asymmetric relationship among factors: the rotated loadings for a stronger factor can be contaminated by those from a weaker one, but the loadings for a weaker factor is almost free of the impact of those from a stronger one. More importantly, the finding implies that there is no need to use complicated penalties to sparsify the loading estimators. Instead, we adopt a simple screening method to recover the sparsity and construct estimators for various factor strengths. In addition, for sparse weak factor models, we provide a singular value thresholding-based approach to determine the number of factors and establish uniform convergence rates for PC estimators, which complement Bai and Ng (2023). The accuracy and efficiency of the proposed estimators are investigated via Monte Carlo simulations. The application to the FRED-QD dataset reveals the underlying factor strengths and loading sparsity as well as their dynamic features.
Cost-benefit of green infrastructures for water management: A sustainability assessment of full-scale constructed wetlands in Northern and Southern Italy
Authors: Laura Garcia-Herrero, Stevo Lavrnic, Valentina Guerrieri, Attilio Toscano, Mirco Milani, Giuseppe Luigi Cirelli, Matteo Vittuari
Date: 2023-05-10 00:00:00
Tasks: Management

Sustainable water management has become an urgent challenge due to irregular water availability patterns and water quality issues. The effect of climate change exacerbates this phenomenon in water-scarce areas, such as the Mediterranean region, stimulating the implementation of solutions aiming to mitigate or improve environmental, social, and economic conditions. A novel solution inspired by nature, technology-oriented, explored in the past years, is constructed wetlands. Commonly applied for different types of wastewater due to its low cost and simple maintenance, they are considered a promising solution to remove pollutants while creating an improved ecosystem by increasing biodiversity around them. This research aims to assess the sustainability of two typologies of constructed wetlands in two Italian areas: Sicily, with a vertical subsurface flow constructed wetland, and Emilia Romagna, with a surface flow constructed wetland. The assessment is performed by applying a cost-benefit analysis combining primary and secondary data sources. The analysis considered the market and non-market values in both proposed scenarios to establish the feasibility of the two options and identify the most convenient one. Results show that both constructed wetlands bring more benefits (benefits-cost ratio, BCR) than costs (BCR > 0). In the case of Sicily, the BCR is lower (1) in the constructed wetland scenario, while in its absence it is almost double. If other ecosystem services are included the constructed wetland scenario reach a BCR of 4 and a ROI of 5, showing a better performance from a costing perspective than the absence one. In Emilia Romagna, the constructed wetland scenario shows a high BCR (10) and ROI (9), while the scenario in absence has obtained a negative present value indicating that the cost do not cover the benefits expected.
A spectral approach to stock market performance
Authors: Ignacio Escanuela Romana, Clara Escanuela Nieves
Date: 2023-05-09 00:00:00

We pose the estimation and predictability of stock market performance. Three cases are taken: US, Japan, Germany, the monthly index of the value of realized investment in stocks, prices plus the value of dividend payments (OECD data). Once deflated and trend removed, harmonic analysis is applied. The series are taken with and without the periods with evidence of exogenous shocks. The series are erratic and the random walk hypothesis is reasonably falsified. The estimation reveals relevant hidden periodicities, which approximate stock value movements. From July 2008 onwards, it is successfully analyzed whether the subsequent fall in share value would have been predictable. Again, the data are irregular and scattered, but the sum of the first five harmonics in relevance anticipates the fall in stock market values that followed.
Multiple Adjusted Quantiles
Authors: Christopher P. Chambers, Alan D. Miller
Date: 2023-05-10 00:00:00

We cardinally and ordinally rank distribution functions (CDFs). We present a new class of statistics, maximal adjusted quantiles, and show that a statistic is invariant with respect to cardinal shifts, preserves least upper bounds with respect to the first order stochastic dominance relation, and is lower semicontinuous if and only if it is a maximal adjusted quantile. A dual result is provided, as are ordinal results. Preservation of least upper bounds is given several interpretations, including one that relates to changes in tax brackets, and one that relates to valuing options composed of two assets.
Yquilibrium: A Theory for (Non-) Convex Economies
Authors: Jacob K Goeree
Date: 2023-05-10 00:00:00

General equilibrium, the cornerstone of modern economics and finance, rests on assumptions many markets do not meet. Spectrum auctions, electricity markets, and cap-and-trade programs for resource rights often feature non-convexities in preferences or production that can cause non-existence of Walrasian equilibrium and render general equilibrium vacuous. Yquilibrium complements general equilibrium with an optimization approach to (non-) convex economies that does not require perfect competition. Yquilibrium coincides with Walrasian equilibrium when the latter exists. Yquilibrium exists even if Walrasian equilibrium ceases to and produces optimal allocations subject to linear, anonymous, and (approximately) utility-clearing prices.
Robust Regulation of Firms' Access to Consumer Data
Authors: Jose Higueras
Date: 2023-05-10 00:00:00

In this paper I study how to regulate firms' access to consumer data when the latter is used for price discrimination and the regulator faces non-Bayesian uncertainty about the correlation structure between data and willingness to pay, and hence about the way the monopolist will use the consumers' information to segment the market. I fully characterize all policies that are worst-case optimal when the regulator maximizes consumer surplus: the regulator allows the monopolist to access data if the monopolist cannot use the database to identify a small group of consumers. Furthermore, from the set of policies that achieve the largest worst-case consumer surplus, I identify the ones that are undominated; i.e., there is no alternative policy that never yields lower consumer surplus, and sometimes strictly higher consumer surplus.
Self-Supervised Federated Learning for Fast MR Imaging
Authors: Juan Zou, Cheng Li, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang
Date: 2023-05-10 00:00:00
Tasks: Federated Learning, Image Reconstruction

Federated learning (FL) based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multi-site institutions without violating patient's privacy for accelerating MR imaging. However, existing methods rely on fully sampled data for collaborative training of the model. The client that only possesses undersampled data can neither participate in FL nor benefit from other clients. Furthermore, heterogeneous data distributions hinder FL from training an effective deep learning reconstruction model and thus cause performance degradation. To address these issues, we propose a Self-Supervised Federated Learning method (SSFedMRI). SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data. Furthermore, a personalized soft update scheme is designed to simultaneously capture the global shared representations among different centers and maintain the specific data distribution of each client. The proposed method is evaluated on four datasets and compared to the latest state-of-the-art approaches. Experimental results demonstrate that SSFedMRI possesses strong capability in reconstructing accurate MR images both visually and quantitatively on both in-distribution and out-of-distribution datasets.
Image Segmentation For Improved Lossless Screen Content Compression
Authors: Shabhrish Reddy Uddehal, Tilo Strutz, Hannah Och, André Kaup
Date: 2023-05-10 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two sub images (natural and synthetic parts), and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.
Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility
Authors: Thomas Wilding, Benjamin J. B. Deutschmann, Christian Nelson, Xuhong LI, Fredrik Tufvesson, Klaus Witrisal
Date: 2023-05-10 00:00:00
Tasks: Super-Resolution

This paper deals with propagation and channel modeling for physically large arrays. The focus lies on acquiring a spatially consistent model, which is essential, especially for positioning and sensing applications. Ultra-wideband, synthetic array measurement data have been acquired with large positioning devices to support this research. We present a modified multipath channel model that accounts for a varying visibility of multipath components along a large array. Based on a geometric model of the measurement environment, we analyze the visibility of specular components. We show that, depending on the size of the reflecting surface, geometric visibility and amplitude estimates obtained with a super-resolution channel estimation algorithm show a strong correspondence. Furthermore, we highlight the capabilities of the developed synthetic array measurement system.
Asymptotic behavior of an epidemic model with infinitely many variants
Authors: Jean-Baptiste Burie, Arnaud Ducrot, Quentin Griette
Date: 2023-05-09 00:00:00

We investigate the long-time dynamics of a SIR epidemic model with infinitely many pathogen variants infecting a homogeneous host population. We show that the basic reproduction number $\mathcal{R}_0$ of the pathogen can be defined in that case and corresponds to a threshold between the persistence ($\mathcal{R}_0>1$) and the extinction ($\mathcal{R}_0\leq 1$) of the pathogen. When $\mathcal{R}_0>1$ and the maximal fitness is attained by at least one variant, we show that the systems reaches an equilibrium state that can be explicitly determined from the initial data. When $\mathcal{R}_0>1$ but none of the variants attain the maximal fitness, the situation is more intricate. We show that, in general, the pathogen is uniformly persistent and any family of variants that have a fitness which is uniformly lower than the optimal fitness, eventually gets extinct. We derive a condition under which the total pathogen population converges to a limit which can be computed explicitly. We also find counterexamples that show that, when our condition is not met, the total pathogen population may converge to an unexpected value, or the system can even reach an eternally transient behavior where the total pathogen population between several values. We illustrate our results with numerical simulations that emphasize the wide variety of possible dynamics.