pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous and Robust Orbit-keeping for Small Body Missions
Authors: Rodolfo Batista Negri, Antonio Fernando Bertachini de Almeida Prado
Date: 2023-07-20 00:00:00

This article presents a path-following control law for autonomous orbital maintenance of small body missions. The control law is robust, stable, and capable of controlling only the orbital geometry, allowing the spacecraft to operate with idle-thruster periods. It is entirely analytical and suitable for real-time operations. The control law is inspired by the two-body problem and uses sliding mode control theory to ensure robustness against bounded disturbances. Practical considerations, such as measurement noise, thruster limitations, and hysteresis-based control switching, are taken into account. The proposed control law is demonstrated and validated through several examples, including orbit-keeping around the asteroid Bennu, showing its feasibility and efficiency for small body missions. The results indicate that the control law can achieve precise and safe orbit maintenance with minimal fuel consumption, making it a valuable asset for autonomous space missions.
A Circular Restricted n-body Problem
Authors: Rodolfo Batista Negri, Antonio Fernando Bertachini de Almeida Prado
Date: 2023-07-20 00:00:00

This paper introduces the Circular Restricted n-Body Problem (CRNBP), an extension of the bicircular restricted four-body problem (BCR4BP) designed to describe the dynamics of an n-body system. In the CRNBP, each massive body in the system is constrained to follow a Keplerian motion, similar to the BCR4BP's artificial constraint. The CRNBP is an efficient alternative for trajectory design in multiple-body systems, particularly for outer planetary systems, as it requires integrating only six first-order ordinary differential equations compared to the 6N equations in an ephemerides model. By reproducing complex dynamical behaviors observed in ephemerides n-body problems, we demonstrate the structural stability of the CRNBP. Additionally, we propose a straightforward approach to relate the CRNBP with ephemerides, enabling the exploration of trajectory design possibilities before committing to a dedicated ephemerides analysis. This allows for the identification of general dynamical behaviors and provides valuable insights into the dynamics of multiple body systems. Finally, illustrative examples highlight the richness of trajectories and potential advantages of using the CRNBP for designing complex trajectories in outer planetary systems. The CRNBP proves to be a valuable tool for preliminary trajectory design, facilitating the identification of low-energy trajectories and providing a foundation for further exploration in future dedicated studies.
Set-Point Tracking MPC with Avoidance Features
Authors: Marcelo A. Santos, Antonio Ferramosca, Guilherme V. Raffo
Date: 2023-07-20 00:00:00
Tasks: Model Predictive Control, Point Tracking

This work proposes a finite-horizon optimal control strategy to solve the tracking problem while providing avoidance features to the closed-loop system. Inspired by the set-point tracking model predictive control (MPC) framework, the central idea of including artificial variables into the optimal control problem is considered. This approach allows us to add avoidance features into the set-point tracking MPC strategy without losing the properties of an enlarged domain of attraction and feasibility insurances in the face of any changing reference. Besides, the artificial variables are considered together with an avoidance cost functional to establish the basis of the strategy, maintaining the recursive feasibility property in the presence of a previously unknown number of regions to be avoided. It is shown that the closed-loop system is recursively feasible and input-to-state-stable under the mild assumption that the avoidance cost is uniformly bounded over time. Finally, two numerical examples illustrate the controller behavior.
Parallel Shooting Sequential Quadratic Programming for Nonlinear MPC Problems
Authors: P. C. N. Verheijen, M. Haghi, M. Lazar, D. Goswami
Date: 2023-07-20 00:00:00
Tasks: Model Predictive Control

In this paper, we propose a parallel shooting algorithm for solving nonlinear model predictive control problems using sequential quadratic programming. This algorithm is built on a two-phase approach where we first test and assess sequential convergence over many initial trajectories in parallel. However, if none converge, the algorithm starts varying the Newton step size in parallel instead. Through this parallel shooting approach, it is expected that the number of iterations to converge to an optimal solution can be decreased. Furthermore, the algorithm can be further expanded and accelerated by implementing it on GPUs. We illustrate the effectiveness of the proposed Parallel Shooting Sequential Quadratic Programming (PS-SQP) method in some benchmark examples for nonlinear model predictive control. The developed PS-SQP parallel solver converges faster on average and especially when significant nonlinear behaviour is excited in the NMPC horizon.
Implicit predictors in regularized data-driven predictive control
Authors: Manuel Klädtke, Moritz Schulze Darup
Date: 2023-07-20 00:00:00

We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace predictive control). To demonstrate this concept, we derive and analyze implicit predictors for some basic data-driven predictive control (DPC) schemes, which offers a new perspective on this popular approach that may form the basis for modified DPC schemes and further theoretical insights.
Individualization of atrial tachycardia models for clinical applications: Performance of fiber-independent model
Authors: Jiyue He, Arkady Pertsov, John Bullinga, Rahul Mangharam
Date: 2023-07-20 00:00:00

One of the challenges in the development of patient-specific models of cardiac arrhythmias for clinical applications has been accounting for myocardial fiber organization. The fiber varies significantly from heart to heart, but cannot be directly measured in live tissue. The goal of this paper is to evaluate in-silico the accuracy of left atrium activation maps produced by a fiber-independent (isotropic) model with tuned diffusion coefficients, compares to a model incorporating myocardial fibers with the same geometry. For this study we utilize publicly available DT-MRI data from 7 ex-vivo hearts. The comparison is carried out in 51 cases of focal and rotor arrhythmias located in different regions of the atria. On average, the local activation time accuracy is 96% for focal and 93% for rotor arrhythmias. Given its reasonably good performance and the availability of readily accessible data for model tuning in cardiac ablation procedures, the fiber-independent model could be a promising tool for clinical applications.
Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology
Authors: Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li
Date: 2023-07-19 00:00:00

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need to consider kinetic features and signal signatures, such as micro-Doppler, in ATR techniques to efficiently recognize small drones. The authors also present a comprehensive drone detection radar system design that balances detection and tracking requirements, incorporating parameter adjustment based on scattering region theory. They offer an example of a performance improvement achieved using feedback and situational awareness mechanisms with the integrated ATR capabilities. Furthermore, the paper examines challenges related to one-way attack drones and explores the potential of cognitive radar as a solution. The integration of ATR capabilities transforms a 3D radar system into a 4D radar system, resulting in improved drone detection performance. These advancements are useful in military, civilian, and commercial applications, and ongoing research and development efforts are essential to keep radar systems effective and ready to detect, track, and respond to emerging threats.
Solvable Neural Network Model for Input-Output Associations: Optimal Recall at the Onset of Chaos
Authors: Tomoki Kurikawa, Kunihiko Kaneko
Date: 2023-07-11 00:00:00
Tasks: Sensitivity

In neural information processing, an input modulates neural dynamics to generate a desired output. To unravel the dynamics and underlying neural connectivity enabling such input-output association, we proposed an exactly soluble neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs. An analytic form of the response upon the input is derived, whereas three distinctive types of responses including chaotic dynamics as bifurcation against input strength are obtained depending on the neural sensitivity and number of inputs. Optimal performance is achieved at the onset of chaos, and the relevance of the results to cognitive dynamics is discussed.
Reinforcement Learning for Photonic Component Design
Authors: Donald Witt, Jeff Young, Lukas Chrostowski
Date: 2023-07-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers fabricated on an air clad 220 nm silicon on insulator single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm can cover a 150 nm bandwidth with less than 10.2 dB of loss at their lowest point.
Stochastic trade-offs and the emergence of diversification in E. coli evolution experiments
Authors: Roberto Corral López, Samir Suweis, Sandro Azaele, Miguel A. Muñoz
Date: 2023-07-20 00:00:00

Laboratory experiments with bacterial colonies, under well-controlled conditions often lead to evolutionary diversification, where at least two ecotypes emerge from an initially monomorphic population. Empirical evidence suggests that such ''evolutionary branching'' occurs stochastically, even under fixed and stable conditions. This stochastic nature is characterized by: (i) occurrence in a significant fraction, but not all, of experimental settings, (ii) emergence at widely varying times, and (iii) variable relative abundances of the resulting subpopulations across experiments. Theoretical approaches to understanding evolutionary branching under these conditions have been previously developed within the (deterministic) framework of ''adaptive dynamics''. Here, we advance the understanding of the stochastic nature of evolutionary outcomes by introducing the concept of ''stochastic trade-offs'' as opposed to ''hard'' ones. The key idea is that the stochasticity of mutations occurs in a high-dimensional trait space and this translates into variability that is constrained to a flexible tradeoff curve. By incorporating this additional source of stochasticity, we are able to account for the observed empirical variability and make predictions regarding the likelihood of evolutionary branching under different conditions. This approach effectively bridges the gap between theoretical predictions and experimental observations, providing insights into when and how evolutionary branching is more likely to occur in laboratory experiments.
Selected Topics of Social Physics: Nonequilibrium Systems
Authors: V. I. Yukalov
Date: 2023-07-20 00:00:00

This review article is the second part of the project ``Selected Topics of Social Physics". The first part has been devoted to equilibrium systems. The present part considers nonequilibrium systems. The style of the paper combines the features of a tutorial and a review, which, from one side, makes it easy to read for nonspecialists aiming at grasping the basics of social physics, and from the other side, describes several rather recent original models containing new ideas that could be of interest to experienced researchers in the field. The present material is based on the lectures that the author had been giving during several years at the Swiss Federal Institute of Technology in Zurich (ETH Zurich).
Analysis of the rate of force development reveals high neuromuscular fatigability in elderly patients with chronic kidney disease
Authors: Antoine Chatrenet, Giorgina Piccoli, Jean Michel Audebrand, Massimo Torreggiani, Julien Barbieux, Charly Vaillant, Baptiste Morel, Sylvain Durand, Bruno Beaune
Date: 2023-07-20 00:00:00

Background Chronic kidney disease (CKD) induces muscle wasting and a reduction in the maximum voluntary force (MVF). Little is known about the neuromuscular fatigability in CKD patients, defined as the reduction of muscle force capacities during exercise. Neuromuscular fatigability is a crucial physical parameter of the daily living. The quantification of explosive force has been shown to be a sensitive means to assess neuromuscular fatigability. Thus, our study used explosive force estimates to assess neuromuscular fatigability in elderly CKD patients. Methods Inclusion criteria for CKD patients were age $\ge$ 60 years old and glomerular filtration rate (GFR) < 45 mL/ min/1.73 m 2 not on dialysis, and those for controls were GFR > 60 mL/min/1.73 m 2 , age and diabetes matched. The fatigability protocol focused on a handgrip task coupled with surface electromyography (sEMG). Scalars were extracted from the rate of force development (RFD): absolute and normalized time periods (50, 75, 100, 150 and 200 ms, RFD 50 , RFD 75 , RFD 100 , RFD 150 and RFD 200 , respectively), peak RFD (RFD peak in absolute; NRFD peak normalized), timeto-peak RFD (t-RFD peak) and the relative force at RFD peak (MVF-RFD peak). A statistical parametric mapping approach was performed on the force, impulse and RFD-time curves. The integrated sEMG with time at 0-30, 0-50, 0-100 and 0-200 ms time intervals relative to onset of sEMG activity was extracted and groups were compared separately for each sex. Results The cohort of 159 individuals had a median age of 69 (9 IQR) years and body mass index was 27.6 (6.2 IQR) kg/ m 2. Propensity-score-matched groups balanced CKD patients and controls by gender with 66 males and 34 females. In scalar analysis, CKD patients manifested a higher decrement than controls in the early phase of contraction, regarding the NRFD peak (P = 0.009; $\eta$ 2 p = 0.034) and RFD 75 and RFD 100 (for both P < 0.001; $\eta$ 2 p = 0.068 and 0.064). The onedimensional analysis confirmed that CKD males manifest higher and delayed neuromuscular fatigability, especially before 100 ms from onset of contraction. sEMG was lower in CKD patients than controls in the 0-100 ms (at rest: P = 0.049, Cohen's d = 0.458) and 0-200 ms (at rest: P = 0.016, Cohen's d = 0.496; during exercise: P = 0.006, Cohen's d = 0.421) time windows. Controls showed greater decrease of sEMG than CKD patients in the 0-30 ms (P = 0.020, Cohen's d = 0.533) and 0-50 ms (P = 0.010, Cohen's d = 0.640) time windows. As opposite to females, males showed almost the same differences between groups. Conclusions Our study is the first to show that CKD patients have higher fatigability than controls, which may be associated with an impaired motor-unit recruitment, highlighting a neural drive disturbance with CKD. Further studies are needed to confirm these findings.
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
Authors: Zeyu Tang, Xiaodan Xing, Guang Yang
Date: 2023-07-02 00:00:00
Tasks: Super-Resolution

Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or sinogram reconstruction, which require the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR=49.74 vs. 40.66, p$<$0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR=1.19 and HR=1.14, p$<$0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.
Real-Time Detection of Local No-Arbitrage Violations
Authors: Torben G. Andersen, Viktor Todorov, Bo Zhou
Date: 2023-07-20 00:00:00

This paper focuses on the task of detecting local episodes involving violation of the standard It\^o semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities. Our proposed detectors, defined as stopping rules, are applied sequentially to continually incoming high-frequency data. We show that they are asymptotically exponentially distributed in the absence of Ito semimartingale violations. On the other hand, when a violation occurs, we can achieve immediate detection under infill asymptotics. A Monte Carlo study demonstrates that the asymptotic results provide a good approximation to the finite-sample behavior of the sequential detectors. An empirical application to S&P 500 index futures data corroborates the effectiveness of our detectors in swiftly identifying the emergence of an extreme return persistence episode in real time.
PySDTest: a Python/Stata Package for Stochastic Dominance Tests
Authors: Kyungho Lee, Yoon-Jae Whang
Date: 2023-07-20 00:00:00

We introduce PySDTest, a Python/Stata package for statistical tests of stochastic dominance. PySDTest implements various testing procedures such as Barrett and Donald (2003), Linton et al. (2005), Linton et al. (2010), and Donald and Hsu (2016), along with their extensions. Users can flexibly combine several resampling methods and test statistics, including the numerical delta method (D\"umbgen, 1993; Hong and Li, 2018; Fang and Santos, 2019). The package allows for testing advanced hypotheses on stochastic dominance relations, such as stochastic maximality among multiple prospects. We first provide an overview of the concepts of stochastic dominance and testing methods. Then, we offer practical guidance for using the package and the Stata command pysdtest. We apply PySDTest to investigate the portfolio choice problem between the daily returns of Bitcoin and the S&P 500 index as an empirical illustration. Our findings indicate that the S&P 500 index returns second-order stochastically dominate the Bitcoin returns.
Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method
Authors: Sebastian Calcetero-Vanegas, Andrei L. Badescu, X. Sheldon Lin
Date: 2023-07-05 00:00:00

Claim reserving primarily relies on macro-level models, with the Chain-Ladder method being the most widely adopted. These methods were heuristically developed without minimal statistical foundations, relying on oversimplified data assumptions and neglecting policyholder heterogeneity, often resulting in conservative reserve predictions. Micro-level reserving, utilizing stochastic modeling with granular information, can improve predictions but tends to involve less attractive and complex models for practitioners. This paper aims to strike a practical balance between aggregate and individual models by introducing a methodology that enables the Chain-Ladder method to incorporate individual information. We achieve this by proposing a novel framework, formulating the claim reserving problem within a population sampling context. We introduce a reserve estimator in a frequency and severity distribution-free manner that utilizes inverse probability weights (IPW) driven by individual information, akin to propensity scores. We demonstrate that the Chain-Ladder method emerges as a particular case of such an IPW estimator, thereby inheriting a statistically sound foundation based on population sampling theory that enables the use of granular information, and other extensions.
Indicatori comuni del PNRR e framework SDGs: una proposta di indicatore composito
Authors: Fabio Bacchini, Lorenzo Di Biagio, Giampiero M. Gallo, Vincenzo Spinelli
Date: 2023-07-20 00:00:00

The main component of the NextGeneration EU (NGEU) program is the Recovery and Resilience Facility (RRF), spanning an implementation period between 2021 and 2026. The RRF also includes a monitoring system: every six months, each country is required to send an update on the progress of the plan against 14 common indicators, measured on specific quantitative scales. The aim of this paper is to present the first empirical evidence on this system, while, at the same time, emphasizing the potential of its integration with the sustainable development framework (SDGs). We propose to develop a first linkage between the 14 common indicators and the SDGs which allows us to produce a composite index (SDGs-RRF) for France, Germany, Italy, and Spain for the period 2014-2021. Over this time, widespread improvements in the composite index across the four countries led to a partial reduction of the divergence. The proposed approach represents a first step towards a wider use of the SDGs for the assessment of the RRF, in line with their use in the European Semester documents prepared by the European Commission.
Commitment and the Dynamics of Household Labor Supply
Authors: Alexandros Theloudis, Jorge Velilla, Pierre-André Chiappori, J. Ignacio Giménez-Nadal, José Alberto Molina
Date: 2023-07-20 00:00:00

The extent to which individuals commit to their partner for life has important implications. This paper develops a lifecycle collective model of the household, through which it characterizes behavior in three prominent alternative types of commitment: full, limited, and no commitment. We propose a test that distinguishes between all three types based on how contemporaneous and historical news affect household behavior. Our test permits heterogeneity in the degree of commitment across households. Using recent data from the Panel Study of Income Dynamics, we reject full and no commitment, while we find strong evidence for limited commitment.
Subjective Expected Utility and Psychological Gambles
Authors: Gianluca Cassese
Date: 2023-07-19 00:00:00

We obtain an elementary characterization of expected utility based on a representation of choice in terms of psychological gambles, which requires no assumption other than coherence between ex-ante and ex-post preferences. Weaker version of coherence are associated with various attitudes towards complexity and lead to a characterization of minimax or Choquet expected utility.