pwc-archive/papers-with-abstracts huggingface dataset:
Equilibrium Selection in Replicator Equations Using Adaptive-Gain Control
Authors: Lorenzo Zino, Mengbin Ye, Giuseppe Carlo Calafiore, Alessandro Rizzo
Date: 2024-07-12 00:00:00

In this paper, we deal with the equilibrium selection problem, which amounts to steering a population of individuals engaged in strategic game-theoretic interactions to a desired collective behavior. In the literature, this problem has been typically tackled by means of open-loop strategies, whose applicability is however limited by the need of accurate a priori information on the game and scarce robustness to uncertainty and noise. Here, we overcome these limitations by adopting a closed-loop approach using an adaptive-gain control scheme within a replicator equation -a nonlinear ordinary differential equation that models the evolution of the collective behavior of the population. For most classes of 2-action matrix games we establish sufficient conditions to design a controller that guarantees convergence of the replicator equation to the desired equilibrium, requiring limited a-priori information on the game. Numerical simulations corroborate and expand our theoretical findings.
A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S)
Authors: Daniel Landgraf, Andreas Völz, Knut Graichen
Date: 2024-07-12 00:00:00
Tasks: Model Predictive Control

This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state and can consider unknown parts of the system dynamics using Gaussian process regression. These methods are used to reformulate a stochastic MPC formulation as a deterministic one that is solved with GRAMPC. The performance of the presented framework is evaluated using examples from a wide range of technical areas. The experimental evaluation shows that GRAMPC-S can be used in practice for the control of nonlinear uncertain systems with sampling times in the millisecond range.
Codification of Good Seamanship in Complex and Congested Waterways
Authors: Yaqub Aris Prabowo, Peter Nicholas Hansen, Dimitrios Papageorgiou, Roberto Galeazzi
Date: 2024-07-12 00:00:00

This paper presents a novel method to quantify seafarers' good seamanship during navigation scenarios with multi-vessel encounters -- in open and confined waters --, and to compute COLREG's-compliant trajectories for avoiding collision and grounding. The quantification of good seamanship requires knowledge about the state of the vessels (position, heading, and speed) and the surrounding sailing environment. Such information is accessible through the AIS system and the electronic nautical chart. The proposed method evaluates mutual collision risk by examining domain violations of each vessel, and comparing them to the seaman's actions. This results in a comprehensive metric of good seamanship. As risk free actions are not always possible in the resolution of a potential collision and grounding, the method adopts a branch-and-bound scheme to identify achievable maneuvers that minimize the risk. Further, the dynamic nature of vessel speed in congested scenarios is considered, recognizing potential changes in both own and target vessels' forward speeds. The proposed method is experimentally evaluated using historical AIS data and sea charts of Danish waters. This research contributes to the field by providing a more realistic perspective on seamanship in complex maritime environments.
Perceived Time To Collision as Public Space Users' Discomfort Metric
Authors: Alireza Jafari, Yen-Chen Liu
Date: 2024-07-12 00:00:00
Tasks: Position

Micro-mobility transport vehicles such as e-scooters are joining current sidewalk users and affect the safety and comfort of pedestrians as primary sidewalk users. The lack of agreed-upon metrics to quantify people's discomfort hinders shared public space safety research. We introduce perceived Time To Collision (TTC) as a potential metric of user discomfort performing controlled experiments using an e-scooter and a pedestrian moving in a hallway. The results strongly correlate the participant's reported discomfort and the perceived TTC. Therefore, TTC is a potential metric for public space users' discomfort. Since the metric only uses relative velocity and position information, it is a viable candidate for neighboring people's discomfort estimation in advanced driver assistance systems for e-scooters and PMVs. Our ongoing research extends the results to mobile robots.
Dynamic Modeling and Stability Analysis of Balancing in Riderless Electric Scooters
Authors: Yun-Hao Lin, Alireza Jafari, Yen-Chen Liu
Date: 2024-07-12 00:00:00

Today, electric scooter is a trendy personal mobility vehicle. The rising demand and opportunities attract ride-share services. A common problem of such services is abandoned e-scooters. An autonomous e-scooter capable of moving to the charging station is a solution. This paper focuses on maintaining balance for these riderless e-scooters. The paper presents a nonlinear model for an e-scooter moving with simultaneously varying speed and steering. A PD and a feedback-linearized PD controller stabilize the model. The stability analysis shows that the controllers are ultimately bounded even with parameter uncertainties and measurement inaccuracy. Simulations on a realistic e-scooter with a general demanding path to follow verify the ultimate boundedness of the controllers. In addition, the feedback-linearized PD controller outperforms the PD controller because it has narrower ultimate bounds. Future work focuses on experiments using a self-balancing mechanism installed on an e-scooter.
Real-time Pipe Burst Localization in Water Distribution Networks Using Change Point Detection Algorithms
Authors: Takudzwa Mzembegwa, Clement N Nyirenda
Date: 2024-07-12 00:00:00
Tasks: Change Point Detection

Change point detection (CPD) has proved to be an effective tool for detecting drifts in data and its use over the years has become more pronounced due to the vast amount of data and IoT devices readily available. This study analyzes the effectiveness of Cumulative Sum (CUSUM) and Shewhart Control Charts for identifying the occurrence of abrupt pressure changes for pipe burst localization in Water Distribution Network (WDN). Change point detection algorithms could be useful for identifying the nodes that register the earliest and most drastic pressure changes with the aim of detecting pipe bursts in real-time. TSNet, a Python package, is employed in order to simulate pipe bursts in a WDN. The pressure readings are served to the pipe burst localization algorithm the moment they are available for real-time pie burst localization. The performance of the pipe burst localization algorithm is evaluated using a key metric such as localization accuracy under different settings to compare its performance when paired with either CUSUM or Shewhart. Results show that the pipe burst localization algorithm has an overall better performance when paired with CUSUM. Although, it does show great accuracy for both CPD algorithms when pressure readings are being continuously made available without a big gap between time steps. The proposed approach however still needs further experiments on different WDNs to assess the performance and accuracy of the algorithm on real-world WDN models.
Energy-Based Control Approaches for Weakly Coupled Electromechanical Systems
Authors: N. Javanmardi, P. Borja, M. J. Yazdanpanah, J. M. A. Scherpen
Date: 2024-07-11 00:00:00

This paper addresses the regulation and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamiltonian framework. Then, we employ Lyapunov theory and the notion of contractive systems to develop control approaches in the port-Hamiltonian framework. Remarkably, these control methods eliminate the need for solving partial differential equations or implementing any change of coordinates and are endowed with a physical interpretation. We also investigate the effect of coupled damping on the transient performance and convergence rate of the closed-loop system. Finally, the applicability of the proposed approaches is illustrated in two applications of electromechanical systems via simulations.
An Optimal Task Planning and Agent-aware Allocation Algorithm in Collaborative Tasks Combining with PDDL and POPF
Authors: Qiguang Chen, Ya-Jun Pan
Date: 2024-07-11 00:00:00
Tasks: Task Planning

Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to generate optimal plans for collaborative robots and human workers to replace rigid, hard-coded production line plans in industrial scenarios. This will be achieved by integrating the Planning Domain Definition Language (PDDL), Partial Order Planning Forwards (POPF) task planner, and a task allocation algorithm. The task allocation algorithm proposed in this paper generates a cost function for general actions in the industrial scenario, such as PICK, PLACE, and MOVE, by considering practical factors such as feasibility, reachability, safety, and cooperation level for both robots and human agents. The actions and costs will then be translated into a language understandable by the planning system using PDDL and fed into POPF solver to generate an optimal action plan. In the end, experiments are conducted where assembly tasks are executed by a collaborative system with two manipulators and a human worker to test the feasibility of the theory proposed in this paper.
Improved Model and Analysis for RIS-Assisted Indoor Terahertz Wireless Networks
Authors: Zhi Chai, Jiajie Xu, Mohamed-Slim Alouini, Justin P. Coon
Date: 2024-07-11 00:00:00

In this paper, we propose a new model for indoor THz communication assisted by RIS. We conduct a realistic modeling of indoor obstacles and analyze their impact on performance. Order statistics are applied to calculate the cumulative distribution functions (CDFs) of distances from the transmitter to the selected RIS, i.e., the nearest RIS in the bounded indoor environment to the transmitter, and from the selected RIS to the receiver. We calculate the coverage probability (CP) as a function of RIS number, obstacle density, room size, and the transmitter's location. By comparing the numerical results obtained from the analytical expressions with Monte Carlo simulations, we verify the accuracy of our analysis. Through numerical results, it is observed that room size and obstacle density affect the CP in a significant way. However, by optimizing the transmitter's location and increasing the RIS number deployed in the room, the CP can be significantly improved (e.g., an increase of around 15% by optimizing the transmitter's location, and an increase of around 30% by increasing the RIS number deployed in the room).
Integrated User Matching and Pricing in Round-Trip Car-Sharing
Authors: Avalpreet Singh Brar, Rong Su, Gioele Zardini, Jaskaranveer Kaur
Date: 2024-07-11 00:00:00

Traditional round-trip car rental systems mandate users to return vehicles to their point of origin, limiting the system adaptability to meet diverse mobility demands. This constraint often leads to fleet under-utilization and incurs high parking costs for idle vehicles. To address this inefficiency, we propose a N-user matching algorithm which is designed to facilitate one-way trips within the round-trip rental framework. Our algorithm addresses the joint problem of optimal pricing and user matching through a Two-Stage Integer Linear Programming (ILP)-based formulation. In the first stage, optimal rental prices are determined by setting a risk factor that governs the likelihood of matching a set of N-user. The second stage involves maximizing expected profit through a novel ILP-based user-matching formulation. Testing our algorithm on real-world scenarios demonstrates an approximate 35\% increase in demand fulfillment. Additionally, we assess the model robustness under uncertainty by varying factors such as the risk factor (probability of user ride acceptance at the offered price), cost factor (rental cost-to-fare ratio), and maximum chain length.
Performance-Barrier Event-Triggered Control of a Class of Reaction-Diffusion PDEs
Authors: Bhathiya Rathnayake, Mamadou Diagne, Jorge Cortes, Miroslav Krstic
Date: 2024-07-11 00:00:00

We employ the recent performance-barrier event-triggered control (P-ETC) for achieving global exponential convergence of a class of reaction-diffusion PDEs via PDE backstepping control. Rather than insisting on a strictly monotonic decrease of the Lyapunov function for the closed-loop system, P-ETC allows the Lyapunov function to increase as long as it remains below an acceptable performance-barrier. This approach integrates a performance residual, the difference between the value of the performance-barrier and the Lyapunov function, into the triggering mechanism. The integration adds flexibility and results in fewer control updates than with regular ETC (R-ETC) that demands a monotonic decrease of the Lyapunov function. Our P-ETC PDE backstepping design ensures global exponential convergence of the closed-loop system in the spatial L^2 norm, without encountering Zeno phenomenon. To avoid continuous monitoring of the triggering function that generates events, we develop periodic event-triggered and self-triggered variants (P-PETC and P-STC, respectively) of the P-ETC. The P-PETC only requires periodic evaluation of the triggering function whereas the P-STC preemptively computes the time of the next event at the current event time using the system model and continuously available system states. The P-PETC and P-STC also ensure a Zeno-free behavior and deliver performance equivalent to that of the continuous-time P-ETC which requires continuous evaluation of the triggering function, in addition to the continuous sensing of the state. We provide numerical simulations to illustrate the proposed technique and to compare it with R-ETC associated with strictly decreasing Lyapunov functions.
The Self-Organized Criticality Paradigm in Economics & Finance
Authors: Jean-Philippe Bouchaud
Date: 2024-07-14 00:00:00

``Self-Organised Criticality'' (SOC) is the mechanism by which complex systems spontaneously settle close to a *critical point*, at the edge between stability and chaos, and characterized by fat-tailed fluctuations and long-memory correlations. Such a scenario may explain why insignificant perturbations can generate large disruptions, through the propagation of ``avalanches'' across the system. In this short review, we discuss how SOC could offer a plausible solution to the excess volatility puzzle in financial markets and the analogue ``small shocks, large business cycle puzzle'' for the economy at large, as initially surmised by Per Bak et al. in 1993. We argue that in general the quest for efficiency and the necessity of *resilience* may be mutually incompatible and require specific policy considerations.
Nonequilibrium Thermodynamics of Non-Ideal Reaction-Diffusion Systems: Implications for Active Self-Organization
Authors: Francesco Avanzini, Timur Aslyamov, Étienne Fodor, Massimiliano Esposito
Date: 2024-07-12 00:00:00

We develop a framework describing the dynamics and thermodynamics of open non-ideal reaction-diffusion systems, which embodies Flory-Huggins theories of mixtures and chemical reaction network theories. Our theory elucidates the mechanisms underpinning the emergence of self-organized dissipative structures in these systems. It evaluates the dissipation needed to sustain and control them, discriminating the contributions from each reaction and diffusion process with spatial resolution. It also reveals the role of the reaction network in powering and shaping these structures. We identify particular classes of networks in which diffusion processes always equilibrate within the structures, while dissipation occurs solely due to chemical reactions. The spatial configurations resulting from these processes can be derived by minimizing a kinetic potential, contrasting with the minimization of the thermodynamic free energy in passive systems. This framework opens the way to investigating the energetic cost of phenomena such as liquid-liquid phase separation, coacervation, and the formation of biomolecular condensates.
A mean field analysis of the role of indirect transmission in emergent infection events
Authors: Tomás Ignacio González, María Fabiana Laguna, Guillermo Abramson
Date: 2024-07-12 00:00:00

We developed a mathematical model to investigate the role of indirect transmission in the spread of infectious diseases, using the illustrative example of sarcoptic mange as a case study. This disease can be transmitted through direct contact between an infected host and a susceptible one, or indirectly when potential hosts encounter infectious mites and larvae deposited in the environment, commonly referred to as fomites. Our focus is on exploring the potential of these infectious reservoirs as triggers for emerging infection events and as stable reservoirs of the disease. To achieve this, our mean field compartmental model incorporates the epidemiological dynamics driven by indirect transmission via fomites. We identify different types of dynamics that the system can go into, controlled by different levels of direct and indirect transmission. Among these, we find a new regime where the disease can emerge and persist over time solely through fomites, without the necessity for direct transmission. This possibility of the system reveals an evolutionary pathway that could enable the parasite to enhance its fitness beyond host co-evolution. We also define a new threshold based on an effective reproductive number, that enables us to predict the conditions for disease persistence. Our model allows us to assess the potential effectiveness of various disease intervention measures by incorporating a feature observed in real systems. We hope this contributes to a better understanding of infectious disease outbreaks.
Dominance to egalitarian transition in diverse communities
Authors: David A. Kessler, Nadav M. Shnerb
Date: 2024-07-11 00:00:00

Diverse communities of competing species are generally characterized by substantial niche overlap and strongly stochastic dynamics. Abundance fluctuations are proportional to population size, so the dynamics of rare populations is slower. Hence, once a population becomes rare, its abundance gets stuck at low values. Here, we analyze the effect of this phenomenon on community structure. We identify two different phases: a dominance phase, in which a tiny number of species constitute most of the community, and an egalitarian phase, where it takes a finite fraction of all species to constitute most of the community. We demonstrate the validity of the theory using empirical findings for a variety of hyperdiverse communities, and clarify the role of demographic stochasticity in shaping patterns of commonness and rarity.
Advances in the Simulation and Modeling of Complex Systems using Dynamical Graph Grammars
Authors: Eric Medwedeff, Eric Mjolsness
Date: 2024-07-14 00:00:00
Tasks: Computational Efficiency

The Dynamical Graph Grammar (DGG) formalism can describe complex system dynamics with graphs that are mapped into a master equation. An exact stochastic simulation algorithm may be used, but it is slow for large systems. To overcome this problem, an approximate spatial stochastic/deterministic simulation algorithm, which uses spatial decomposition of the system's time-evolution operator through an expanded cell complex (ECC), was previously developed and implemented for a cortical microtubule array (CMA) model. Here, computational efficiency is improved at the cost of introducing errors confined to interactions between adjacent subdomains of different dimensions, realized as some events occurring out of order. A rule instances to domains mapping function $\phi$, ensures the errors are local. This approach has been further refined and generalized in this work. Additional efficiency is achieved by maintaining an incrementally updated match data structure for all possible rule matches. The API has been redesigned to support DGG rules in general, rather than for one specific model. To demonstrate these improvements in the algorithm, we have developed the Dynamical Graph Grammar Modeling Library (DGGML) and a DGG model for the periclinal face of the plant cell CMA. This model explores the effects of face shape and boundary conditions on local and global alignment. For a rectangular face, different boundary conditions reorient the array between the long and short axes. The periclinal CMA DGG demonstrates the flexibility and utility of DGGML, and these new methods highlight DGGs' potential for testing, screening, or generating hypotheses to explain emergent phenomena.
Mitigating Interpretation Bias in Rock Records with Large Language Models: Insights from Paleoenvironmental Analysis
Authors: Luoqi Wang, Haipeng Li, Linshu Hu, Jiarui Cai, Zhenhong Du
Date: 2024-05-17 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

The reconstruction of Earth's history faces significant challenges due to the nonunique interpretations often derived from rock records. The problem has long been recognized but there are no systematic solutions in practice. This study introduces an innovative approach that leverages Large Language Models (LLMs) along with retrieval augmented generation and real-time search capabilities to counteract interpretation biases, thereby enhancing the accuracy and reliability of geological analyses. By applying this framework to sedimentology and paleogeography, we demonstrate its effectiveness in mitigating interpretations biases through the generation and evaluation of multiple hypotheses for the same data, which can effectively reduce human bias. Our research illuminates the transformative potential of LLMs in refining paleoenvironmental studies and extends their applicability across various sub-disciplines of Earth sciences, enabling a deeper and more accurate depiction of Earth's evolution.
The Dynamic, the Static, and the Weak: Factor models and the analysis of high-dimensional time series
Authors: Matteo Barigozzi, Marc Hallin
Date: 2024-07-15 00:00:00
Tasks: Time Series

Several fundamental and closely interconnected issues related to factor models are reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak factors, the concept of weakly common component recently introduced by Gersing et al. (2023), the irrelevance of cross-sectional ordering and the assumption of cross-sectional exchangeability, the impact of undetected strong factors, and the problem of combining common and idiosyncratic forecasts. Conclusions all point to the advantages of the General Dynamic Factor Model approach of Forni et al. (2000) over the widely used Static Approximate Factor Model introduced by Chamberlain and Rothschild (1983).