pwc-archive/papers-with-abstracts huggingface dataset:
Computational Validation of a Mathematical Model of Stable Multi-Species Communities in a Hawk Dove Game
Authors: Jeffrey Lu
Date: 2023-04-03 00:00:00

We revisit the original hawk-dove game with slight modifications to payoff values while maintaining the fundamental principles of interaction. The practical robustness of the theoretical tools of game theory is tested on a simulated population of hawks and doves with varying initial population distributions and peak growth rates. Additionally, we aim to find conditions in which the entire community fails or becomes a single-species population. The results show that the predicted community distribution is established by the majority of communities but fails to exist in communities with extreme initial imbalances in species distribution and insufficient growth rates. We also find that greater growth rates can compensate for more imbalanced initial conditions and that more balanced initial conditions can compensate for lower growth rates. Overall, the simple theoretical model is a strong predictor of the stable behavior of simulated multi-species communities.
Decentralized Attack Search and the Design of Bug Bounty Schemes
Authors: Hans Gersbach, Akaki Mamageishvili, Fikri Pitsuwan
Date: 2023-03-31 00:00:00

Systems and blockchains often have security vulnerabilities and can be attacked by adversaries, with potentially significant negative consequences. Therefore, infrastructure providers increasingly rely on bug bounty programs, where external individuals probe the system and report any vulnerabilities (bugs) in exchange for rewards (bounty). We develop a simple contest model of bug bounty. A group of individuals of arbitrary size is invited to undertake a costly search for bugs. The individuals differ with regard to their abilities, which we capture by different costs to achieve a certain probability to find bugs if any exist. Costs are private information. We study equilibria of the contest and characterize the optimal design of bug bounty schemes. In particular, the designer can vary the size of the group of individuals invited to search, add a paid expert, insert an artificial bug with some probability, and pay multiple prizes.
Optimal Resource Allocation between Two Nonfully Cooperative Wireless Networks under Malicious Attacks: A Gestalt Game Perspective
Authors: Yukang Cui, Xinru Yang, TingWen Huang, Xin Gong
Date: 2023-03-22 00:00:00

In this paper, the problem of seeking optimal distributed resource allocation (DRA) policies on cellular networks in the presence of an unknown malicious adding-edge attacker is investigated. This problem is described as the games of games (GoG) model. Specifically, two subnetwork policymakers constitute a Nash game, while the confrontation between each subnetwork policymaker and the attacker is captured by a Stackelberg game. First, we show that the communication resource allocation of cellular networks based on the Foschini-Miljanic (FM) algorithm can be transformed into a \emph{geometric program} and be efficiently solved via convex optimization. Second, the upper limit of attack magnitude that can be tolerated by the network is calculated by the corresponding theory, and it is proved that the above geometric programming (GP) framework is solvable within the attack bound, that is, there exists a Gestalt Nash equilibrium (GNE) in our GoG. Third, a heuristic algorithm that iteratively uses GP is proposed to identify the optimal policy profiles of both subnetworks, for which asymptotic convergence is also confirmed. Fourth, a greedy heuristic adding-edge strategy is developed for the attacker to determine the set of the most vulnerable edges. Finally, simulation examples illustrate that the proposed theoretical results are robust and can achieve the GNE. It is verified that the transmission gains and interference gains of all channels are well tuned within a limited budget, despite the existence of malicious attacks.
Multi-User Data Detection in Massive MIMO with 1-Bit ADCs
Authors: Amin Radbord, Italo Atzeni, Antti Tölli
Date: 2023-03-31 00:00:00

We provide new analytical results on the uplink data detection in massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The statistical properties of the soft-estimated symbols (i.e., after linear combining and prior to the data detection process) have been previously characterized only for a single user equipment (UE) and uncorrelated Rayleigh fading. In this paper, we consider a multi-UE setting with correlated Rayleigh fading, where the soft-estimated symbols are obtained by means of maximum ratio combining based on imperfectly estimated channels. We derive a closed-form expression of the expected value of the soft-estimated symbols, which allows to understand the impact of the specific data symbols transmitted by the interfering UEs. Building on this result, we design efficient data detection strategies based on the minimum distance criterion, which are compared in terms of symbol error rate and complexity.
Machine-Learned Premise Selection for Lean
Authors: Bartosz Piotrowski, Ramon Fernández Mir, Edward Ayers
Date: 2023-03-17 00:00:00

We introduce a machine-learning-based tool for the Lean proof assistant that suggests relevant premises for theorems being proved by a user. The design principles for the tool are (1) tight integration with the proof assistant, (2) ease of use and installation, (3) a lightweight and fast approach. For this purpose, we designed a custom version of the random forest model, trained in an online fashion. It is implemented directly in Lean, which was possible thanks to the rich and efficient metaprogramming features of Lean 4. The random forest is trained on data extracted from mathlib -- Lean's mathematics library. We experiment with various options for producing training features and labels. The advice from a trained model is accessible to the user via the suggest_premises tactic which can be called in an editor while constructing a proof interactively.
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Authors: Dominik Baumann, Thomas B. Schön
Date: 2023-04-02 00:00:00
Tasks: Scheduling, State Estimation

Event-based methods carefully select when to transmit information to enable high-performance control and estimation over resource-constrained communication networks. However, they come at a cost. For instance, event-based communication induces a higher computational load and increases the complexity of the scheduling problem. Thus, in some cases, allocating available slots to agents periodically in circular order may be superior. In this article, we discuss, for a specific example, when the additional complexity of event-based methods is beneficial. We evaluate our analysis in a synthetical example and on 20 simulated cart-pole systems.
Prediction-Based Leader-Follower Rendezvous Model Predictive Control with Robustness to Communication Losses
Authors: Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas, Bo Wahlberg
Date: 2023-04-03 00:00:00
Tasks: Model Predictive Control

In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm is executing DMPC with exchange of trajectories between the agents at a sufficient rate. In the case of loss of communication, and given the sensor setup, agents are predicting the trajectories of other agents based on the available measurements and prior information. The predictions are then used as the reference inputs to DMPC. During the landing, the followers are tasked with avoidance of USV-dependent obstacles and inter-agent collisions. In the proposed distributed algorithm, all agents solve their local optimization problem in parallel and we prove the convergence of the proposed algorithm. Finally, the simulation results support the theoretical findings.
Automated Formation Control Synthesis from Temporal Logic Specifications
Authors: Shuhao Qi, Zengjie Zhang, Sofie Haesaert, Zhiyong Sun
Date: 2023-04-01 00:00:00
Tasks: Navigate

In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks. We propose a framework for a multi-robot swarm to fulfill complex tasks represented by temporal logic specifications. Given temporal logic specifications on the swarm formation and navigation, we develop a controller with runtime safety and convergence guarantees that drive the swarm to formally satisfy the specification. In addition, the synthesized controller will autonomously switch formations as necessary and react to uncontrollable events from the environment. The efficacy of the proposed framework is validated with a simulation study on the navigation of multiple quadrotor robots.
Evaluation of Noise Reduction Methods for Sentence Recognition by Sinhala Speaking Listeners
Authors: Malitha Gunawardhana, Chathuki Navanjana, Dinithi Fernando, Nipuna Upeksha, Anjula De Silva
Date: 2023-03-31 00:00:00
Tasks: Action Detection, Activity Detection, Sentence

Noise reduction is a crucial aspect of hearing aids, which researchers have been striving to address over the years. However, most existing noise reduction algorithms have primarily been evaluated using English. Considering the linguistic differences between English and Sinhala languages, including variation in syllable structures and vowel duration, it is very important to assess the performance of noise reduction tailored to the Sinhala language. This paper presents a comprehensive analysis between wavelet transformation and adaptive filters for noise reduction in Sinhala languages. We investigate the performance of ten wavelet families with soft and hard thresholding methods against adaptive filters with Normalized Least Mean Square, Least Mean Square Average Normalized Least Mean Square, Recursive Least Square, and Adaptive Filtering Averaging optimization algorithms along with cepstral and energy-based voice activity detection algorithms. The performance evaluation is done using objective metrics; Signal to Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ) and a subjective metric; Mean Opinion Score (MOS). A newly recorded Sinhala language audio dataset and the NOIZEUS database by the University of Texas, Dallas were used for the evaluation. Our code is available at https://github.com/ChathukiKet/Evaluation-of-Noise-Reduction-Methods
Hazard Analysis for Self-Adaptive Systems Using System-Theoretic Process Analysis
Authors: Simon Diemert, Jens H. Weber
Date: 2023-04-01 00:00:00
Tasks: Autonomous Vehicles

Self-adaptive systems are able to change their behaviour at run-time in response to changes. Self-adaptation is an important strategy for managing uncertainty that is present during the design of modern systems, such as autonomous vehicles. However, assuring the safety of self-adaptive systems remains a challenge, particularly when the adaptations have an impact on safety-critical functions. The field of safety engineering has established practices for analyzing the safety of systems. System Theoretic Process and Analysis (STPA) is a hazard analysis method that is well-suited for self-adaptive systems. This paper describes a design-time extension of STPA for self-adaptive systems. Then, it derives a reference model and analysis obligations to support the STPA activities. The method is applied to three self-adaptive systems described in the literature. The results demonstrate that STPA, when used in the manner described, is an applicable hazard analysis method for safety-critical self-adaptive systems.
Resource Allocation in IRS-aided Optical Wireless Communication Systems
Authors: Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-04-03 00:00:00

One of the main challenges facing optical wireless communication (OWC) systems is service disconnection in high blockage probability scenarios where users might lose the line of sight (LoS) connection with their corresponding access points (APs). In this work, we study the deployment of passive reflecting surfaces referred to as Intelligent Reflecting Surfaces (IRSs) in indoor visible light communication (VLC) to boost users signal to noise ratio (SNR) and ensure service continuity. We formulate an optimization problem to allocate APs and the mirrors of IRSs to users such that the sum rate is increased. The results show a 35% increase in the sum rate of the IRS-aided OWC system compared to the sum rate achieved by only considering the LoS channel components. The results also shows that the deployment of IRSs improves the sum rate under LoS blockage.
Port-Hamiltonian Modelling for Analysis and Control of Gas Networks
Authors: Albertus J. Malan, Lukas Rausche, Felix Strehle, Sören Hohmann
Date: 2023-04-03 00:00:00

In this paper, we present finite-dimensional port-Hamiltonian system (PHS) models of a gas pipeline and a network comprising several pipelines for the purpose of control design and stability analysis. Starting from the partial differential Euler equations describing the dynamical flow of gas in a pipeline, the method of lines is employed to obtain a lumped-parameter model, which simplifies to a nonlinear third-order PHS. Parallels between gas networks and power systems are drawn by showing that the obtained pipeline PHS model has the same pi-representation as electrical transmission lines. Moreover, to assist future control design, additional passivity properties of the pipeline PHS model are analysed and discussed. By comparing the proposed PHS models against other models in a standard simulation, we show that the simplifying assumptions have no material effect on the model fidelity. The proposed pipeline and network models can serve as a basis for passivity-based control and analysis while the power system parallels facilitate the transfer of existing methods.
Optimal control of quasi-1D Bose gases in optical box potentials
Authors: Andreas Deutschmann-Olek, Katharina Schrom, Nikolaus Würkner, Jörg Schmiedmayer, Sebastian Erne, Andreas Kugi
Date: 2023-04-03 00:00:00

In this paper, we investigate the manipulation of quasi-1D Bose gases that are trapped in a highly elongated potential by optimal control methods. The effective meanfield dynamics of the gas can be described by a one-dimensional non-polynomial Schr\"odinger equation. We extend the indirect optimal control method for the Gross-Pitaevskii equation by Winckel and Borzi (2008) to obtain necessary optimality conditions for state and energy cost functionals. This approach is then applied to optimally compress a quasi-1D Bose gase in an (optical) box potential, i.e., to find a so-called short-cut to adiabaticity, by solving the optimality conditions numerically. The behavior of the proposed method is finally analyzed and compared to simple direct optimization strategies using reduced basis functions. Simulations results demonstrate the feasibility of the proposed approach.
Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions
Authors: Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil Prokhorov, Dimitra Panagou
Date: 2023-04-03 00:00:00

We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing literature, we deviate from the martingale theory that is currently used in stochastic CBF techniques and prove that a RA-CBF based control synthesis confers a tighter upper bound on the probability of the system becoming unsafe within a finite time interval than existing approaches. We highlight the advantages of our proposed approach over the state-of-the-art via a comparative study on an mobile-robot example, and further demonstrate its viability on an autonomous vehicle highway merging problem in dense traffic.
Anti-windup coordination strategy around a fair equilibrium in resource sharing networks
Authors: Felix Agner, Pauline Kergus, Anders Rantzer, Sophie Tarbouriech, Luca Zaccarian
Date: 2023-04-03 00:00:00

This paper aims at coordinating interconnected agents where the control input of each agent is limited by the control input of others. In that sense, the systems have to share a limited resource over a network. Such problems can arise in different areas and it is here motivated by a simplified district heating example. When the shared resource is insufficient for the combined need of all systems, the resource will have to be shared in an optimal fashion. In this scenario, we want the systems to automatically converge to an optimal equilibrium. The contribution of this paper is the proposal of a control architecture where each separate system is controlled by a local PI controller. The controllers are then coordinated through a global rank-one anti-windup signal. It is shown that the equilibrium of the proposed closed-loop system minimizes the infinity-norm of stationary state deviations. Convergence is investigated via a randomized numerical study, and a district heating-inspired example illustrates the utility of the proposed method.
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
Authors: Antoine P. Leeman, Jerome Sieber, Samir Bennani, Melanie N. Zeilinger
Date: 2023-04-03 00:00:00

This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The proposed approach jointly optimizes a nominal nonlinear trajectory and an error feedback, requiring minimal offline design effort and offering low conservatism. This is achieved by decomposing the affine-in-the-parameter uncertain nonlinear system into a nominal $\textit{nonlinear}$ system and an uncertain linear time-varying system. Using this decomposition, we can apply established tools from system level synthesis to $\textit{convexly}$ over-bound all uncertainties in the nonlinear optimization problem. Moreover, it enables tight joint optimization of the linearization error bounds, parametric uncertainties bounds, nonlinear trajectory, and error feedback. With this novel controller parameterization, we can formulate a convex constraint to ensure robust performance guarantees for the nonlinear system. The presented method is relevant for numerous applications related to trajectory optimization, e.g., in robotics and aerospace engineering. We demonstrate the performance of the approach and its low conservatism through the simulation example of a post-capture satellite stabilization.
Data-Driven Safe Controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests
Authors: Yu Chen, Chao Shang, Xiaolin Huang, Xiang Yin
Date: 2023-04-03 00:00:00

In this work, we investigate the data-driven safe control synthesis problem for unknown dynamic systems. We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function. To resolve the issue of unknown system dynamic, we follow the existing approach by converting the RCP to a scenario convex program (SCP) by randomly collecting finite samples of system trajectory. However, to improve the sample efficiency to achieve a desired confidence bound, we provide a new posteriori method with validation tests. Specifically, after collecting a set of data for the SCP, we further collect another set of independent \emph{validate data} as posterior information to test the obtained solution. We derive a new overall confidence bound for the safety of the controller that connects the original sample data, the support constraints, and the validation data. The efficiency of the proposed approach is illustrated by a case study of room temperature control. We show that, compared with existing methods, the proposed approach can significantly reduce the required number of sample data to achieve a desired confidence bound.
Nonlinear Negative Imaginary Systems with Switching
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2023-04-03 00:00:00

In this paper, we extend nonlinear negative imaginary (NI) systems theory to switched systems. Switched nonlinear NI systems and switched nonlinear output strictly negative imaginary (OSNI) systems are defined. We show that the interconnection of two switched nonlinear NI systems is still switched nonlinear NI. The interconnection of a switched nonlinear NI system and a switched nonlinear OSNI system is asymptotically stable under some assumptions. This stability result is then illustrated using a numerical example.
Soft-Minimum Barrier Functions for Safety-Critical Control Subject to Actuation Constraints
Authors: Pedram Rabiee, Jesse B. Hoagg
Date: 2023-04-03 00:00:00

This paper presents a new control approach for guaranteed safety (remaining in a safe set) subject to actuator constraints (the control is in a convex polytope). The control signals are computed using real-time optimization, including linear and quadratic programs subject to affine constraints, which are shown to be feasible. The control method relies on a new soft-minimum barrier function that is constructed using a finite-time-horizon prediction of the system trajectories under a known backup control. The main result shows that: (i) the control is continuous and satisfies the actuator constraints, and (ii) a subset of the safe set is forward invariant under the control. We also demonstrate this control on numerical simulations of an inverted pendulum and a double-integrator ground robot.
Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding
Authors: Licio Romao, Ashish R. Hota, Alessandro Abate
Date: 2023-04-02 00:00:00

We present a novel distributionally robust framework for dynamic programming that uses kernel methods to design feedback control policies. Specifically, we leverage kernel mean embedding to map the transition probabilities governing the state evolution into an associated repreducing kernel Hilbert space. Our key idea lies in combining conditional mean embedding with the maximum mean discrepancy distance to construct an ambiguity set, and then design a robust control policy using techniques from distributionally robust optimization. The main theoretical contribution of this paper is to leverage functional analytic tools to prove that optimal policies for this infinite-dimensional min-max problem are Markovian and deterministic. Additionally, we discuss approximation schemes based on state and input discretization to make the approach computationally tractable. To validate the theoretical findings, we conduct an experiment on safe control for thermostatically controlled loads (TCL).
PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms
Authors: Mohannad Alkhraijah, Rachel Harris, Carleton Coffrin, Daniel K. Molzahn
Date: 2023-04-02 00:00:00

This paper presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This paper demonstrates use cases for PowerModelsADA and validates its implementation with multiple OPF formulations.