pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Errors in Controlled Turret System Given Target Location Input from Artificial Intelligence Methods in Automatic Target Recognition
Authors: Matthew Karlson, Heng Ban, Daniel G. Cole, Mai Abdelhakim, Jennifer Forsythe
Date: 2024-08-29 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we assess the movement error of a targeting system given target location data from artificial intelligence (AI) methods in automatic target recognition (ATR) systems. Few studies evaluate the impacts on the accuracy in moving a targeting system to an aimpoint provided in this manner. To address this knowledge gap, we assess the performance of a controlled gun turret system given target location from an object detector developed from AI methods. In our assessment, we define a measure of object detector error and examine the correlations with several standard metrics in object detection. We then statistically analyze the object detector error data and turret movement error data acquired from controlled targeting simulations, as well as their aggregate error, to examine the impact on turret movement accuracy. Finally, we study the correlations between additional metrics and the probability of a hit. The results indicate that AI technologies are a significant source of error to targeting systems. Moreover, the results suggest that metrics such as the confidence score, intersection-over-union, average precision and average recall are predictors of accuracy against stationary targets with our system parameters.
On Fixed-Time Stability for a Class of Singularly Perturbed Systems using Composite Lyapunov Functions
Authors: Michael Tang, Miroslav Krstic, Jorge Poveda
Date: 2024-08-29 00:00:00

Fixed-time stable dynamical systems are capable of achieving exact convergence to an equilibrium point within a fixed time that is independent of the initial conditions of the system. This property makes them highly appealing for designing control, estimation, and optimization algorithms in applications with stringent performance requirements. However, the set of tools available for analyzing the interconnection of fixed-time stable systems is rather limited compared to their asymptotic counterparts. In this paper, we address some of these limitations by exploiting the emergence of multiple time scales in nonlinear singularly perturbed dynamical systems, where the fast dynamics and the slow dynamics are fixed-time stable on their own. By extending the so-called composite Lyapunov method from asymptotic stability to the context of fixed-time stability, we provide a novel class of Lyapunov-based sufficient conditions to certify fixed-time stability in a class of singularly perturbed dynamical systems. The results are illustrated, analytically and numerically, using a fixed-time gradient flow system interconnected with a fixed-time plant and an additional high-order example.
Network-aware Recommender System via Online Feedback Optimization
Authors: Sanjay Chandrasekaran, Giulia De Pasquale, Giuseppe Belgioioso, Florian Dörfler
Date: 2024-08-29 00:00:00
Tasks: Recommendation Systems

Personalized content on social platforms can exacerbate negative phenomena such as polarization, partly due to the feedback interactions between recommendations and the users. In this paper, we present a control-theoretic recommender system that explicitly accounts for this feedback loop to mitigate polarization. Our approach extends online feedback optimization - a control paradigm for steady-state optimization of dynamical systems - to develop a recommender system that trades off users engagement and polarization reduction, while relying solely on online click data. We establish theoretical guarantees for optimality and stability of the proposed design and validate its effectiveness via numerical experiments with a user population governed by Friedkin-Johnsen dynamics. Our results show these "network-aware" recommendations can significantly reduce polarization while maintaining high levels of user engagement.
Analyzing Errors in Controlled Turret System
Authors: Matthew Karlson, Heng Ban, Daniel G. Cole, Mai Abdelhakim, Jennifer Forsythe, John T. Fitzgibbons
Date: 2024-08-29 00:00:00

The purpose of this paper is to characterize aiming errors in controlled weapon systems given target location as input. To achieve this objective, we analyze the accuracy of a controlled weapon system model for stationary and moving targets under different error sources and firing times. First, we develop a mathematical model of a gun turret and use it to design two controllers, a Proportional-Integral-Derivative controller and a Model Predictive controller, which accept the target location input and move the turret to the centroid of the target in simulations. For stationary targets, we analyze the impact of errors in estimating the system's parameters and uncertainty in the aim point measurement. Our results indicate that turret movement is more sensitive to errors in the moment of inertia than the damping coefficient, which could lead to incorrect simulations of controlled turret system accuracy. The results also support the hypothesis that turret movement errors are larger over longer distances of gun turret movement and, assuming no time constraints, accuracy improves the longer one waits to fire; though this may not always be practical in a combat scenario. Additionally, we demonstrate that the integral control component is needed for high accuracy in moving target scenarios.
Asymptotically Stable Data-Driven Koopman Operator Approximation with Inputs using Total Extended DMD
Authors: Louis Lortie, James Richard Forbes
Date: 2024-08-29 00:00:00

The Koopman operator framework can be used to identify a data-driven model of a nonlinear system. Unfortunately, when the data is corrupted by noise, the identified model can be biased. Additionally, depending on the choice of lifting functions, the identified model can be unstable, even when the underlying system is asymptotically stable. This paper presents an approach to reduce the bias in an approximate Koopman model, and simultaneously ensure asymptotic stability, when using noisy data. Additionally, the proposed data-driven modeling approach is applicable to systems with inputs, such as a known forcing function or a control input. Specifically, bias is reduced by using a total least-squares, modified to accommodate inputs in addition to lifted inputs. To enforce asymptotic stability of the approximate Koopman model, linear matrix inequality constraints are augmented to the identification problem. The performance of the proposed method is then compared to the well-known extended dynamic mode decomposition method and to the newly introduced forward-backward extended dynamic mode decomposition method using a simulated Duffing oscillator dataset and experimental soft robot arm dataset.
Turbulent mixing controls fixation of growing antagonistic populations
Authors: Jonathan Bauermann, Roberto Benzi, David R. Nelson, Suraj Shankar, Federico Toschi
Date: 2024-08-22 00:00:00

Unlike coffee and cream that homogenize when stirred, growing micro-organisms (e.g., bacteria, baker's yeast) can actively kill each other and avoid mixing. How do such antagonistic interactions impact the growth and survival of competing strains, while being spatially advected by turbulent flows? By using numerical simulations of a continuum model, we study the dynamics of two antagonistic strains that are dispersed by incompressible turbulent flows in two spatial dimensions. A key parameter is the ratio of the fluid transport time to that of biological reproduction, which determines the winning strain that ultimately takes over the whole population from an initial heterogeneous state. By quantifying the probability and mean time for fixation along with the spatial structure of concentration fluctuations, we demonstrate how turbulence raises the threshold for biological nucleation and antagonism suppresses flow-induced mixing by depleting the population at interfaces. Our work highlights the unusual biological consequences of the interplay of turbulent fluid flows with antagonistic population dynamics, with potential implications for marine microbial ecology and origins of biological chirality.
Brief Synopsis of the Scientific Career of T. R. Hurd
Authors: Matheus R. Grasselli, Lane P. Hughston
Date: 2024-08-29 00:00:00

As an introduction to a Special Issue of International Journal of Theoretical and Applied Finance in Honour of the Memory of Thomas Robert Hurd we present a brief synopsis of Tom Hurd's scientific career and a bibliography of his scientific publications.
State Space Model of Realized Volatility under the Existence of Dependent Market Microstructure Noise
Authors: Toru Yano
Date: 2024-08-30 00:00:00
Tasks: Econometrics

Volatility means the degree of variation of a stock price which is important in finance. Realized Volatility (RV) is an estimator of the volatility calculated using high-frequency observed prices. RV has lately attracted considerable attention of econometrics and mathematical finance. However, it is known that high-frequency data includes observation errors called market microstructure noise (MN). Nagakura and Watanabe[2015] proposed a state space model that resolves RV into true volatility and influence of MN. In this paper, we assume a dependent MN that autocorrelates and correlates with return as reported by Hansen and Lunde[2006] and extends the results of Nagakura and Watanabe[2015] and compare models by simulation and actual data.
Investor behavior and multiscale cross-correlations: Unveiling regime shifts in global financial markets
Authors: Marina Dolfin, George Kapetanios, Leone Leonida, Jose De Leon Miranda
Date: 2024-08-30 00:00:00

We propose an algorithm to capture emergent patterns in the cross-correlations of financial markets, highlighting regime changes on a global scale. In our approach, financial markets are viewed as complex adaptive systems, and multiscale properties and cross-correlations are considered, particularly during stress conditions such as the COVID-19 pandemic, the invasion of Ukraine by Russia in 2022, and Brexit. We investigate whether significant disruptions reflect an imbalance in investment horizons among investors, and we propose a measure based on this imbalance to depict the impact on global financial markets. The detrended cross-correlation cost (DCCC), which is derived from detrended cross-correlation analysis, uses cross-correlations at different timescales to capture variations in investment horizons amid financial uncertainties. Our algorithm, which combines DCCC analysis and the minimum-spanning-tree filtering approach, tracks system interconnectedness and investor imbalances. We tested the DCCC indicator using daily price series of G7, Russian, and Chinese markets over the past decade and found that it increases sharply during ``crash'' periods compared to ``business as usual'' periods. Our empirical results confirm that short-term investment horizons dominate during financial instabilities; this validates our hypothesis and indicates that the DCCC can serve as a leading indicator of shifts in financial-market regimes.
Optimal Strategy in Werewolf Game: A Game Theoretic Perspective
Authors: ST Wang
Date: 2024-08-30 00:00:00

Werewolf game, also known as Mafia game, is a social deduction game that models the conflict between an informed minority (werewolf group) and an uninformed majority (citizen group). This paper explores the optimal strategies of the werewolf game from the perspective of game theory, focusing on cases both with and without prophet. First we examine the existing strategy in game without prophet and propose ``random strategy +", which provides an improved winning probability for the werewolve group. Then we further study the game with prophet, and find the game with prophet can be transformed into a extensive game with complete but imperfect information under a specific rule. We construct a model and design an algorithm to achieve PBE and maximize the citizen group's winning probability. In the end, we examine a property of PBE in game without any restriction.
Pareto's Limits: Improving Inequality Estimates in America, 1917 to 1965
Authors: Vincent Geloso, Alexis Akira Toda
Date: 2024-08-29 00:00:00

American income inequality, generally estimated with tax data, in the 20th century is widely recognized to have followed a U-curve, though debates persist over the extent of this curve, specifically regarding how high the peaks are and how deep the trough is. These debates focus on assumptions about defining income and handling deductions. However, the choice of interpolation methods for using tax authorities' tabular data to estimate the income of the richest centiles -- especially when no micro-files are available -- has not been discussed. This is crucial because tabular data were consistently used from 1917 to 1965. In this paper, we show that there is an alternative to the standard method of Pareto Interpolation (PI). We demonstrate that this alternative -- Maximum Entropy (ME) -- provides more accurate results and leads to significant revisions in the shape of the U-curve of income inequality.
Robust Technology Regulation
Authors: Andrew Koh, Sivakorn Sanguanmoo
Date: 2024-08-30 00:00:00

We analyze how uncertain technologies should be robustly regulated. An agent develops a new technology and, while privately learning about its harms and benefits, continually chooses whether to continue development. A principal, uncertain about what the agent might learn, chooses among dynamic mechanisms (e.g., paths of taxes or subsidies) to influence the agent's choices in different states. We show that learning robust mechanisms -- those which deliver the highest payoff guarantee across all learning processes -- are simple and resemble `regulatory sandboxes' consisting of zero marginal tax on R&D which keeps the agent maximally sensitive to new information up to a hard quota, upon which the agent turns maximally insensitive. Robustness is important: we characterize the worst-case learning process under non-robust mechanisms and show that they induce growing but weak optimism which can deliver unboundedly poor principal payoffs; hard quotas safeguard against this. If the regulator also learns, adaptive hard quotas are robustly optimal which highlights the importance of expertise in regulation.
Why do elites extend property rights: unlocking investment and the switch to public goods
Authors: Alastair Langtry
Date: 2024-08-30 00:00:00

This paper presents a new rationale for a self-interested economic elite voluntarily extending property rights. When agents make endogenous investment decisions, there is a commitment problem. Ex-post, the elite face strong incentives to expropriate investments from the non-elite (who don't have property rights), which dissuades investment. Extending property rights to new groups can resolve this problem, even for those not given property rights, by making public good provision more attractive to the elite. Unlike other models of franchise extensions, extending property rights in this paper does not involve the elite ceding control to others. Rather, it changes the incentives they face. Additionally, adding identity groups to the model shows that an elite faces weaker incentives to resolve the commitment problem when it is part of a minority identity -- identity fragmentation makes it harder for a society to extend property rights.
Robust Robustness
Authors: Ian Ball, Deniz Kattwinkel
Date: 2024-08-29 00:00:00

The maxmin approach to distributional robustness evaluates each mechanism according to its payoff guarantee over all priors in an ambiguity set. We propose a refinement: the guarantee must be approximately satisfied at priors near the ambiguity set (in the weak topology). We call such a guarantee robust. The payoff guarantees from some maxmin-optimal mechanisms in the literature are not robust. We show, however, that over certain standard ambiguity sets (such as continuous moment sets), every mechanism's payoff guarantee is robust. We give a behavioral characterization of our refined robustness notion by imposing a new continuity axiom on maxmin preferences.
Efficient Polarization Demosaicking via Low-cost Edge-aware and Inter-channel Correlation
Authors: Guangsen Liu, Peng Rao, Xin Chen, Yao Li, Haixin Jiang
Date: 2024-08-30 00:00:00
Tasks: CPU, Demosaicking

Efficient and high-fidelity polarization demosaicking is critical for industrial applications of the division of focal plane (DoFP) polarization imaging systems. However, existing methods have an unsatisfactory balance of speed, accuracy, and complexity. This study introduces a novel polarization demosaicking algorithm that interpolates within a three-stage basic demosaicking framework to obtain DoFP images. Our method incorporates a DoFP low-cost edge-aware technique (DLE) to guide the interpolation process. Furthermore, the inter-channel correlation is used to calibrate the initial estimate in the polarization difference domain. The proposed algorithm is available in both a lightweight and a full version, tailored to different application requirements. Experiments on simulated and real DoFP images demonstrate that our two methods have the highest interpolation accuracy and speed, respectively, and significantly enhance the visuals. Both versions efficiently process a 1024*1024 image on an AMD Ryzen 5600X CPU in 0.1402s and 0.2693s, respectively. Additionally, since our methods only involve computational processes within a 5*5 window, the potential for parallel acceleration on GPUs or FPGAs is highly feasible.
Performance Analysis of Pair-wise Symbol Detection in Uplink NOMA-ISaC Systems
Authors: Haofeng Liu, Emad Alsusa, Arafat Al-Dweik
Date: 2024-08-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates the bit error rate (BER) and outage probability performance of integrated sensing and communication (ISaC) in uplink non-orthogonal multiple access (NOMA) based Internet of Things (IoT) systems. Specifically, we consider an ISaC system where the radar signal is designed to be orthogonal to the communication signal over two symbol periods so that its interference on the communication signal is completely eliminated when detecting the data in pairs of consecutive symbols. This is akin to multi-symbol rate NOMA systems except in this case as the radar bears no data, its waveform is manipulated to be orthogonal to the transmitted communication signal. To eliminate potential decision ambiguity during the pair-wise data detection, a constant phase-offset between adjacent communication symbols is applied at the transmitter. The performance of such a system is analyzed through deriving analytical expressions for the exact BER of zero-forcing (ZF) based receivers. In addition, close-form expressions for the upper BER bound and the outage probability for both ZF and the joint maximum likelihood (JML) receivers are presented. The results show that the derived expressions are perfectly matched with the simulation results. The obtained expressions provide an insight into the performance of this novel ISaC system including demonstrating the impact of various parameters and showing how the ZF receiver provides a useful trade-off between performance and complexity relative to the JML receiver.
A Homogeneous Graph Neural Network for Precoding and Power Allocation in Scalable Wireless Networks
Authors: Mingjun Sun, Shaochuan Wu, Haojie Wang, Yuanwei Liu, Guoyu Li, Tong Zhang
Date: 2024-08-30 00:00:00
Tasks: Graph Neural Network

Deep learning is widely used in wireless communications but struggles with fixed neural network sizes, which limit their adaptability in environments where the number of users and antennas varies. To overcome this, this paper introduced a generalization strategy for precoding and power allocation in scalable wireless networks. Initially, we employ an innovative approach to abstract the wireless network into a homogeneous graph. This primarily focuses on bypassing the heterogeneous features between transmitter (TX) and user entities to construct a virtual homogeneous graph serving optimization objectives, thereby enabling all nodes in the virtual graph to share the same neural network. This ``TX entity'' is known as a base station (BS) in cellular networks and an access point (AP) in cell-free networks. Subsequently, we design a universal graph neural network, termed the information carrying graph neural network (ICGNN), to capture and integrate information from this graph, maintaining permutation invariance. Lastly, using ICGNN as the core algorithm, we tailor the neural network's input and output for specific problem requirements and validate its performance in two scenarios: 1) in cellular networks, we develop a matrix-inverse-free multi-user multi-input multi-output (MU-MIMO) precoding scheme using the conjugate gradient (CG) method, adaptable to varying user and antenna numbers; 2) in a cell-free network, facing dynamic variations in the number of users served by APs, the number of APs serving each user, and the number of antennas per AP, we propose a universal power allocation scheme. Simulations demonstrate that the proposed approach not only significantly reduces computational complexity but also achieves, and potentially exceeds, the spectral efficiency (SE) of conventional algorithms.
MPADA: Open source framework for multimodal time series antenna array measurements
Authors: Yuyi Chang, Yingzhe Zhang, Asimina Kiourti, Emre Ertin
Date: 2024-08-29 00:00:00
Tasks: Time Series

This paper presents an open-source framework for collecting time series S-parameter measurements across multiple antenna elements, dubbed MPADA: Multi-Port Antenna Data Acquisition. The core of MPADA relies on the standard SCPI protocol to be compatible with a wide range of hardware platforms. Time series measurements are enabled through the use of a high-precision real-time clock (RTC), allowing MPADA to periodically trigger the VNA and simultaneously acquire other sensor data for synchronized cross-modal data fusion. A web-based user interface has been developed to offer flexibility in instrumentation, visualization, and analysis. The interface is accessible from a broad range of devices, including mobile ones. Experiments are performed to validate the reliability and accuracy of the data collected using the proposed framework. First, we show the framework's capacity to collect highly repeatable measurements from a complex measurement protocol using a microwave tomography imaging system. The data collected from a test phantom attain high fidelity where a position-varying clutter is visible through coherent subtraction. Second, we demonstrate timestamp accuracy for collecting time series motion data jointly from an RF kinematic sensor and an angle sensor. We achieved an average of 11.8 ms MSE timestamp accuracy at a mixed sampling rate of 10 to 20 Hz over a total of 16-minute test data. We make the framework openly available to benefit the antenna measurement community, providing researchers and engineers with a versatile tool for research and instrumentation. Additionally, we offer a potential education tool to engage engineering students in the subject, fostering hands-on learning through remote experimentation.