pwc-archive/papers-with-abstracts huggingface dataset:
Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds
Authors: Rashed Shelim, Walid Saad, Naren Ramakrishnan
Date: 2024-06-15 00:00:00

Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detecting multiple-input multiple-output (MIMO) signals in dynamic communication require large training samples (in the order of hundreds of thousands to millions) and online retraining to adapt to domain shift. Some dynamic networks, such as vehicular networks, cannot tolerate the waiting time associated with gathering a large number of training samples or online fine-tuning which incurs significant end-to-end delay. In this paper, a novel classification technique based on the concept of geodesic flow kernel (GFK) is proposed for MIMO signal detection. In particular, received MIMO signals are first represented as points on Grassmannian manifolds by formulating basis of subspaces spanned by the rows vectors of the received signal. Then, the domain shift is modeled using a geodesic flow kernel integrating the subspaces that lie on the geodesic to characterize changes in geometric and statistical properties of the received signals. The kernel derives low-dimensional representations of the received signals over the Grassman manifolds that are invariant to domain shift and is used in a geometric support vector machine (G-SVM) algorithm for MIMO signal detection in an unsupervised manner. Simulation results reveal that the proposed method achieves promising performance against the existing baselines like OAMPnet and MMNet with only 1,200 training samples and without online retraining.
Risk-Aware Value-Oriented Net Demand Forecasting for Virtual Power Plants
Authors: Yufan Zhang, Jiajun Han, Yuanyuan Shi
Date: 2024-06-14 00:00:00
Tasks: Demand Forecasting, parameter estimation

This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs. At the training phase, a bilevel program for parameter estimation is formulated, where the upper level optimizes over the forecast model parameter to minimize the conditional value-at-risk (a risk metric) of operation costs. The lower level solves the operation problems given the forecast. Leveraging the specific structure of the operation problem, we show that the bilevel program is equivalent to a convex program when the forecast model is linear. Numerical results show that our approach effectively reduces the risk of high costs compared to the forecasting approach developed for risk-neutral decision makers.
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System
Authors: Jonathan Gornet, Mehdi Hosseinzadeh, Bruno Sinopoli
Date: 2024-06-14 00:00:00
Tasks: Multi-Armed Bandits

Online decision-making can be formulated as the popular stochastic multi-armed bandit problem where a learner makes decisions (or takes actions) to maximize cumulative rewards collected from an unknown environment. This paper proposes to model a stochastic multi-armed bandit as an unknown linear Gaussian dynamical system, as many applications, such as bandits for dynamic pricing problems or hyperparameter selection for machine learning models, can benefit from this perspective. Following this approach, we can build a matrix representation of the system's steady-state Kalman filter that takes a set of previously collected observations from a time interval of length $s$ to predict the next reward that will be returned for each action. This paper proposes a solution in which the parameter $s$ is determined via an adaptive algorithm by analyzing the model uncertainty of the matrix representation. This algorithm helps the learner adaptively adjust its model size and its length of exploration based on the uncertainty of its environmental model. The effectiveness of the proposed scheme is demonstrated through extensive numerical studies, revealing that the proposed scheme is capable of increasing the rate of collected cumulative rewards.
Multi-Objective Control Co-design Using Graph-Based Optimization for Offshore Wind Farm Grid Integration
Authors: Himanshu Sharma, Wei Wang, Bowen Huang, Thiagarajan Ramachandran, Veronica Adetola
Date: 2024-06-14 00:00:00

Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, efficiency, and flexibility of the grid. Nevertheless, limited studies have attempted to perform energy storage sizing while including design and operations (i.e., control co-design) for offshore wind farms. As a result, the present work develops a control co-design optimization formulation to optimize multiple objectives and identify Pareto optimal solutions. The graph-based optimization framework is proposed to address the complexity of the system, allowing the optimization problem to be decomposed for large power systems. The IEEE-9 bus system is treated as an onshore AC grid with two offshore wind farms connected via a multi-terminal DC grid for our use case. The developed methodology successfully identifies the Pareto front during the control co-design optimization, enabling decision-makers to select the best compromise solution for multiple objectives.
Learning Nonlinear Reduced Order Models using State-Space Neural Networks with Ordered State Variance
Authors: Midhun T. Augustine, Mani Bhushan, Sharad Bhartiya
Date: 2024-06-14 00:00:00
Tasks: Model Predictive Control, State Estimation

A novel State-Space Neural Network with Ordered variance (SSNNO) is presented in which the state variables are ordered in decreasing variance. A systematic way of model order reduction with SSNNO is proposed, which leads to a Reduced order SSNNO (R-SSNNO). Theoretical results for the existence of an SSNNO with arbitrary bounds on the output prediction error are presented. The application of SSNNO in control: Model Predictive Control (MPC) and state estimation: Extended Kalman Filter (EKF) is discussed. The effectiveness of SSNNO in system identification and control is illustrated using simulations on a nonlinear continuous reactor process example.
Online Identification of Time-Varying Systems Using Excitation Sets and Change Point Detection
Authors: Chi Ho Leung, Ashish R. Hota, Philip E. Paré
Date: 2024-06-14 00:00:00
Tasks: Change Point Detection

In this work, we first show that the problem of parameter identification is often ill-conditioned and lacks the persistence of excitation required for the convergence of online learning schemes. To tackle these challenges, we introduce the notion of optimal and greedy excitation sets which contain data points with sufficient richness to aid in the identification task. We then present the greedy excitation set-based recursive least squares algorithm to alleviate the problem of the lack of persistent excitation, and prove that the iterates generated by the proposed algorithm minimize an auxiliary weighted least squares cost function. When data points are generated from time-varying parameters, online estimators tend to underfit the true parameter trajectory, and their predictability deteriorates. To tackle this problem, we propose a memory resetting scheme leveraging change point detection techniques. Finally, we illustrate the performance of the proposed algorithms via several numerical case studies to learn the (time-varying) parameters of networked epidemic dynamics, and compare it with results obtained using conventional approaches.
Suppressing seizure via optimal electrical stimulation to the hub of epileptic brain network
Authors: Zhichao Liang, Guanyi Zhao, Yinuo Zhang, Weiting Sun, Jingzhe Lin, Jialin Wang, Quanying Liu
Date: 2024-06-14 00:00:00

The electrical stimulation to the seizure onset zone (SOZ) serves as an efficient approach to seizure suppression. Recently, seizure dynamics have gained widespread attendance in its network propagation mechanisms. Compared with the direct stimulation to SOZ, other brain network-level approaches that can effectively suppress epileptic seizures remain under-explored. In this study, we introduce a platform equipped with a system identification module and a control strategy module, to validate the effectiveness of the hub of the epileptic brain network in suppressing seizure. The identified surrogate dynamics show high predictive performance in reconstructing neural dynamics which enables the model predictive framework to achieve accurate neural stimulation. The electrical stimulation on the hub of the epileptic brain network shows remarkable performance as the direct stimulation of SOZ in suppressing seizure dynamics. Underpinned by network control theory, our platform offers a general tool for the validation of neural stimulation.
Two-Step Blackout Mitigation by Flexibility-Enabled Microgrid Islanding
Authors: Philipp Danner, Anna Volkova, Hermann de Meer
Date: 2024-06-14 00:00:00

Blackouts are disastrous events with a low probability of occurrence but a high impact on the system and its users. With the help of more distributed and controllable generation and sector-coupled flexibility, microgrids could be prepared to operate in islanded mode during a blackout. This paper discusses a two-step blackout mitigation approach for highly renewable microgrids that utilizes user flexibility and energy storage systems for power balance in islanded grid operation. The proposed method includes a proactive flexibility reservation step, which derives a minimal reservation schedule for microgrid resources under uncertainty considering related operational costs. As a second step, during a blackout, a fully distributed control is implemented to maximize the usage of available resources based on a sequence of max and min-consensus rounds. This paper focuses on the second step, for which the effectiveness of blackstart and long-term coordination is shown. Load shedding can be reduced by 40\% compared to the forecast value. A hardware-in-the-loop simulation of a grid-forming converter further showed a fast convergence toward the optimal operation point.
Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances
Authors: Ibon Gracia, Dimitris Boskos, Luca Laurenti, Morteza Lahijanian
Date: 2024-06-14 00:00:00

In this paper, we introduce a data-driven framework for synthesis of provably-correct controllers for general nonlinear switched systems under complex specifications. The focus is on systems with unknown disturbances whose effects on the dynamics of the system is nonlinear. The specifications are assumed to be given as linear temporal logic over finite traces (LTLf) formulas. Starting from observations of either the disturbance or the state of the system, we first learn an ambiguity set that contains the unknown distribution of the disturbances with a user-defined confidence. Next, we construct a robust Markov decision process (RMDP) as a finite abstraction of the system. By composing the RMDP with the automaton obtained from the LTLf formula and performing optimal robust value iteration on the composed RMDP, we synthesize a strategy that yields a high probability that the uncertain system satisfies the specifications. Our empirical evaluations on systems with a wide variety of disturbances show that the strategies synthesized with our approach lead to high satisfaction probabilities and validate the theoretical guarantees.
Hierarchical Control for Vehicle Repositioning in Autonomous Mobility on Demand Systems
Authors: Pengbo Zhu, Giancarlo Ferrari-Trecate, Nikolas Geroliminis
Date: 2024-06-13 00:00:00

Balancing passenger demand and vehicle availability is crucial for ensuring the sustainability and effectiveness of urban transportation systems. To address this challenge, we propose a novel hierarchical strategy for the efficient distribution of empty vehicles in urban areas. The proposed approach employs a data-enabled predictive control algorithm to develop a high-level controller, which guides the inter-regional allocation of idle vehicles. This algorithm utilizes historical data on passenger demand and vehicle supply in each region to construct a non-parametric representation of the system, enabling it to determine the optimal number of vehicles to be repositioned or retained in their current regions without modeling the system. At the low level, a coverage control-based controller is designed to provide inter-regional position guidance, determining the desired road intersection each vehicle should target. With the objective of optimizing area coverage, it aligns the vehicle distribution with the demand across different districts within a single region. The effectiveness of the proposed method is validated through simulation experiments on the real road network of Shenzhen, China. The integration of the two layers provides better performance compared to applying either layer in isolation, demonstrating its potential to reduce passenger waiting time and answer more requests, thus promoting the development of more efficient and sustainable transportation systems.
Validation of human benchmark models for Automated Driving System approval: How competent and careful are they really?
Authors: Pierluigi Olleja, Gustav Markkula, Jonas Bärgman
Date: 2024-06-13 00:00:00
Tasks: counterfactual

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations describing the systems' approval and validation procedures are in effect in Europe. The UNECE Regulation 157 (R157) is one of those. Annex 3 of R157 describes two driver models, representing the performance of a "competent and careful" driver, which can be used as benchmarks to determine whether, in certain situations, a crash would be preventable by a human driver. However, these models have not been validated against human behavior in real safety-critical events. Therefore, this study uses counterfactual simulation to assess the performance of the two models when applied to 38 safety-critical cut-in near-crashes from the SHRP2 naturalistic driving study. The results show that the two computational models performed rather differently from the human drivers: one model showed a generally delayed braking reaction compared to the human drivers, causing crashes in three of the original near-crashes. The other model demonstrated, in general, brake onsets substantially earlier than the human drivers, possibly being overly sensitive to lateral perturbations. That is, the first model does not seem to behave as the competent and careful driver it is supposed to represent, while the second seems to be overly careful. Overall, our results show that, if models are to be included in regulations, they need to be substantially improved. We argue that achieving this will require better validation across the scenario types that the models are intended to cover (e.g., cut-in conflicts), a process which should include applying the models counterfactually to near-crashes and validating them against several different safety related metrics.
Enhancing Resilience of Power Systems against Typhoon Threats: A Hybrid Data-Model Driven Approach
Authors: Yang Li
Date: 2024-06-13 00:00:00

This chapter addresses the increasing vulnerability of coastal regions to typhoons and the consequent power outages, emphasizing the critical role of power transmission systems in disaster resilience. It introduces a framework for assessing and enhancing the resilience of these systems against typhoon impacts. The approach integrates a hybrid-driven model for system failure analysis and resilience assessment, employing both data-driven and model-driven techniques. It includes a unique method to identify system vulnerabilities and optimal strategies for resilience enhancement, considering cost-effectiveness. The efficacy of this method is demonstrated through simulations on the IEEE RTS-79 system under realistic typhoon scenarios, showcasing its potential to guide planners in making informed decisions for disaster resilience.
Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence
Authors: Christopher Koh, Laurent Pagnier, Michael Chertkov
Date: 2024-06-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Turbulent diffusion causes particles placed in proximity to separate. We investigate the required swimming efforts to maintain an active particle close to its passively advected counterpart. We explore optimally balancing these efforts by developing a novel physics-informed reinforcement learning strategy and comparing it with prescribed control and physics-agnostic reinforcement learning strategies. Our scheme, coined the actor-physicist, is an adaptation of the actor-critic algorithm in which the neural network parameterized critic is replaced with an analytically derived physical heuristic function, the physicist. We validate the proposed physics-informed reinforcement learning approach through extensive numerical experiments in both synthetic BK and more realistic Arnold-Beltrami-Childress flow environments, demonstrating its superiority in controlling particle dynamics when compared to standard reinforcement learning methods.
Catalytic evolution of cooperation in a population with behavioural bimodality
Authors: Anhui Sheng, Jing Zhang, Guozhong Zheng, Jiqiang Zhang, Weiran Cai, Li Chen
Date: 2024-06-17 00:00:00
Tasks: Q-Learning

The remarkable adaptability of humans in response to complex environments is often demonstrated by the context-dependent adoption of different behavioral modes. However, the existing game-theoretic studies mostly focus on the single-mode assumption, and the impact of this behavioral multimodality on the evolution of cooperation remains largely unknown. Here, we study how cooperation evolves in a population with two behavioral modes. Specifically, we incorporate Q-learning and Tit-for-Tat (TFT) rules into our toy model, where prisoner's dilemma game is played and we investigate the impact of the mode mixture on the evolution of cooperation. While players in Q-learning mode aim to maximize their accumulated payoffs, players within TFT mode repeat what their neighbors have done to them. In a structured mixing implementation where the updating rule is fixed for each individual, we find that the mode mixture greatly promotes the overall cooperation prevalence. The promotion is even more significant in the probabilistic mixing, where players randomly select one of the two rules at each step. Finally, this promotion is robust when players are allowed to adaptively choose the two modes by real-time comparison. In all three scenarios, players within the Q-learning mode act as catalyzer that turns the TFT players to be more cooperative, and as a result drive the whole population to be highly cooperative. The analysis of Q-tables explains the underlying mechanism of cooperation promotion, which captures the ``psychologic evolution" in the players' mind. Our study indicates that the variety of behavioral modes is non-negligible, and could be crucial to clarify the emergence of cooperation in the real world.
Extinction in agent-based and collective models of bet-hedging
Authors: Manuel Dávila-Romero, Francisco J. Cao-García, Luis Dinis
Date: 2024-06-17 00:00:00

Bet-hedging is a phenotype diversification strategy that combines a fast-growing vulnerable phenotype with a slow-growing resistant phenotype. In environments switching between favorable and unfavorable conditions, bet-hedging optimizes growth and reduces fluctuations over a long time, which is expected to reduce extinction risk. Here, we address directly how bet-hedging can reduce extinction probability in an agent-based model. An agent-based model is appropriate for studying extinction due to the low number of individuals close to extinction. We also show that the agent-based model converges to the collective model behavior for populations of $100$ individuals or more. However, the collective model provides relevant qualitative insight even for low populations. The collective model provides expressions for extinction that stress the relevance of the population number, showing that a factor four increase in the total population has a greater effect than a change of strategy from maximum growth to minimal extinction. This work provides further insight into the impact of finite population effects on the bet-hedging strategy's success.
A stochastic field theory for the evolution of quantitative traits in finite populations
Authors: Ananda Shikhara Bhat
Date: 2024-06-15 00:00:00

Infinitely many distinct trait values may arise in populations bearing quantitative traits, and modeling their population dynamics is thus a formidable task. While classical models assume fixed or infinite population size, models in which the total population size fluctuates due to demographic noise in births and deaths can behave qualitatively differently from constant or infinite population models due to density-dependent dynamics. In this paper, I present a stochastic field theory for the eco-evolutionary dynamics of finite populations bearing one-dimensional quantitative traits. I derive stochastic field equations that describe the evolution of population densities, trait frequencies, and the mean value of any trait in the population. These equations recover well-known results such as the replicator-mutator equation, Price equation, and gradient dynamics in the infinite population limit. For finite populations, the equations describe the intricate interplay between natural selection, noise-induced selection, eco-evolutionary feedback, and neutral genetic drift in determining evolutionary trajectories. My work uses ideas from statistical physics, calculus of variations, and SPDEs, providing alternative methods that complement the measure-theoretic martingale approach that is more common in the literature.
Vegetation clustering and self-organization in inhomogeneous environments
Authors: D. Pinto-Ramos, M. G. Clerc, A. Makhoute, M. Tlidi
Date: 2024-06-18 00:00:00
Tasks: Clustering

Due to climatic changes, excessive grazing, and deforestation, semi-arid and arid ecosystems are vulnerable to desertification and land degradation. Adversely affected biological productivity has a negative impact on social, economic, and environmental factors. The term 'arid ecosystems' refers not only to water-scarce landscapes but also to nutrient-poor environments. Specifically, the vegetation cover loses spatial homogeneity as aridity increases, and the self-organized heterogeneous vegetation patterns developed could eventually collapse into a bare state. It is still unclear whether this transition would be gradual or abrupt, leading to the often-called catastrophic shift of ecosystems. Several studies suggest that environmental inhomogeneities in time or space can promote a gradual transition to bare soil, thus avoiding catastrophic shifts. Environmental inhomogeneities include non-uniformities in the spatial distribution of precipitation, spatial irregularities in topography and other external factors. Employing a generic mathematical model including environmental inhomogeneities in space, we show how two branches of vegetation patterns create a hysteresis loop when the effective mortality level changes. These two branches correspond to qualitatively distinct vegetation self-organized responses. In an increasing mortality scenario, one observes an equilibrium branch of high vegetation biomass forming self-organized patterns with a well-defined wavelength. However, reversing the mortality trend, one observes a low biomass branch lacking a wavelength. We call this phenomenon the clustering of vegetation patches. This behavior can be connected to historically significant trends of climate change in arid ecosystems.