pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning
Authors: Justin Kong, Terrence J. Moore, Fikadu T. Dagefu
Date: 2024-01-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This letter investigates covert routing communications in a heterogeneous network where a source transmits confidential data to a destination with the aid of relaying nodes where each transmitter judiciously chooses one modality among multiple communication modalities. We develop a novel reinforcement learning-based covert routing algorithm that finds a route from the source to the destination where each node identifies its next hop and modality only based on the local feedback information received from its neighboring nodes. We show based on numerical simulations that the proposed covert routing strategy has only negligible performance loss compared to the optimal centralized routing scheme.
Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology Communication
Authors: Shumin Yao, Xiaodong Xu, Hao Chen, Yaping Sun, Qinglin Zhao
Date: 2024-01-26 00:00:00
Tasks: Decoder

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63\% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.
An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
Authors: Ayad Ghany Ismaeel, S. J. Jereesha Mary, C. Anitha, Jaganathan Logeshwaran, Sarmad Nozad Mahmood, Sameer Alani, Akram H. Shather
Date: 2024-01-24 00:00:00
Tasks: Management

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.
Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations
Authors: Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi, Claire Tomlin, Andrea Bajcsy
Date: 2024-02-15 00:00:00

Autonomous agents should be able to coordinate with other agents without knowing their intents ahead of time. While prior work has studied how agents can gather information about the intent of others, in this work, we study the inverse problem: how agents can demonstrate their intent to others, within the framework of general-sum dynamic games. We first present a model of this intent demonstration problem and then propose an algorithm that enables an agent to trade off their task performance and intent demonstration to improve the overall system's performance. To scale to continuous states and action spaces as well as to nonlinear dynamics and costs, our algorithm leverages linear-quadratic approximations with an efficient intent teaching guarantee. Our empirical results show that intent demonstration accelerates other agents' learning and enables the demonstrating agent to balance task performance with intent expression.
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Authors: Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu
Date: 2024-02-15 00:00:00

Solving nonlinear optimal control problems is a challenging task, particularly for high-dimensional problems. We propose algorithms for model-based policy iterations to solve nonlinear optimal control problems with convergence guarantees. The main component of our approach is an iterative procedure that utilizes neural approximations to solve linear partial differential equations (PDEs), ensuring convergence. We present two variants of the algorithms. The first variant formulates the optimization problem as a linear least square problem, drawing inspiration from extreme learning machine (ELM) for solving PDEs. This variant efficiently handles low-dimensional problems with high accuracy. The second variant is based on a physics-informed neural network (PINN) for solving PDEs and has the potential to address high-dimensional problems. We demonstrate that both algorithms outperform traditional approaches, such as Galerkin methods, by a significant margin. We provide a theoretical analysis of both algorithms in terms of convergence of neural approximations towards the true optimal solutions in a general setting. Furthermore, we employ formal verification techniques to demonstrate the verifiable stability of the resulting controllers.
Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
Authors: Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
Date: 2024-02-15 00:00:00

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example.
Quickest Detection of False Data Injection Attack in Distributed Process Tracking
Authors: Saqib Abbas Baba, Arpan Chattopadhyay
Date: 2024-02-15 00:00:00
Tasks: Change Detection

This paper addresses the problem of detecting false data injection (FDI) attacks in a distributed network without a fusion center, represented by a connected graph among multiple agent nodes. Each agent node is equipped with a sensor, and uses a Kalman consensus information filter (KCIF) to track a discrete time global process with linear dynamics and additive Gaussian noise. The state estimate of the global process at any sensor is computed from the local observation history and the information received by that agent node from its neighbors. At an unknown time, an attacker starts altering the local observation of one agent node. In the Bayesian setting where there is a known prior distribution of the attack beginning instant, we formulate a Bayesian quickest change detection (QCD) problem for FDI detection in order to minimize the mean detection delay subject to a false alarm probability constraint. While it is well-known that the optimal Bayesian QCD rule involves checking the Shriyaev's statistic against a threshold, we demonstrate how to compute the Shriyaev's statistic at each node in a recursive fashion given our non-i.i.d. observations. Next, we consider non-Bayesian QCD where the attack begins at an arbitrary and unknown time, and the detector seeks to minimize the worst case detection delay subject to a constraint on the mean time to false alarm and probability of misidentification. We use the multiple hypothesis sequential probability ratio test for attack detection and identification at each sensor. For unknown attack strategy, we use the window-limited generalized likelihood ratio (WL-GLR) algorithm to solve the QCD problem. Numerical results demonstrate the performances and trade-offs of the proposed algorithms.
Repurposing Coal Power Plants into Thermal Energy Storage for Supporting Zero-carbon Data Centers
Authors: Yifu Ding, Serena Patel, Dharik Mallapragada, Robert James Stoner
Date: 2024-02-15 00:00:00

Coal power plants will need to be phased out and face stranded asset risks under the net-zero energy system transition. Repurposing coal power plants could recoup profits and reduce carbon emissions using the existing infrastructure and grid connections. This paper investigates a retrofitting strategy that turns coal power plants into thermal energy storage (TES) and zero-carbon data centers (DCs). The proposed capacity expansion model considers the co-locations of DCs, local renewablewith the system-generation, andlevel coal retir energy storage ement and retrofitting. We optimize the DC system configurations under the hourly-matching carbon policy and flexible operations. Results show that under hourly-matching carbon constraints, the retrofitted TES could complement the operations of lithium-ion batteries (LIBs) to reduce system costs. This could render DCs with optimal co-located renewable generations and energy storage more cost-effective than unconstrained DCs.
Dynamic modeling and predictive control of a microfluidic system
Authors: Jorge Vicente Martinez, Edgar Ramirez-Laboreo, Pablo Calderon Gil
Date: 2024-02-14 00:00:00

Microfluidics, the study of fluids in microscopic channels, has led to important advances in fields as diverse as microelectronics, biotechnology and chemistry. Microfluidic research is primarily based on the use of microfluidic chips, low-cost devices that can be used to perform laboratory experiments using small amounts of fluid. These systems, however, require advanced control mechanisms in order to accurately achieve the flow rates and pressures required in the experiments. In this paper, we present the design of a model predictive controller intended to regulate the fluid flows in one of these systems. The results obtained, both through simulations and real experiments performed on the device, show that predictive control is an ideal technique to control these systems, especially taking into account all the existing constraints.
Managing Household Waste through Transfer Learning
Authors: Suman Kunwar
Date: 2024-01-27 00:00:00
Tasks: Classification, Transfer Learning

As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification.
Online Mean Estimation for Multi-frame Optical Fiber Signals On Highways
Authors: LinLin Wang, Mingxue Quan, Wei Wang, Dezhao Wang, Shanwen Wang
Date: 2024-01-20 00:00:00
Tasks: regression

In the era of Big Data, prompt analysis and processing of data sets is critical. Meanwhile, statistical methods provide key tools and techniques to extract valuable insights and knowledge from complex data sets. This paper creatively applies statistical methods to the field of traffic, particularly focusing on the preprocessing of multi-frame signals obtained by optical fiber-based Distributed Acoustic Sensing (DAS) system. An online non-parametric regression model based on Local Polynomial Regression (LPR) and variable bandwidth selection is employed to dynamically update the estimation of mean function as signals flow in. This mean estimation method can derive average information of multi-frame fiber signals, thus providing the basis for the subsequent vehicle trajectory extraction algorithms. To further evaluate the effectiveness of the proposed method, comparison experiments were conducted under real highway scenarios, showing that our approach not only deals with multi-frame signals more accurately than the classical filter-based Kalman and Wavelet methods, but also meets the needs better under the condition of saving memory and rapid responses. It provides a new reliable means for signal processing which can be integrated with other existing methods.
Self-Healing Effects in OAM Beams Observed on a 28 GHz Experimental Link
Authors: Marek Klemes, Lan Hu, Greg Bowles, Mohammad Akbari, Soulideth Thirakoune, Michael Schwartzman, Kevin Zhang, Tan Huy Ho, David Wessel, Wen Tong
Date: 2024-02-07 00:00:00

In this paper we document for the first time some of the effects of self-healing, a property of orbital-angular-momentum (OAM) or vortex beams, as observed on a millimeter-wave experimental communications link in an outdoors line-of-sight (LOS) scenario. The OAM beams have a helical phase and polarization structure and have conical amplitude shape in the far field. The Poynting vectors of the OAM beams also possess helical structures, orthogonal to the corresponding helical phase-fronts. Due to such non-planar structure in the direction orthogonal to the beam axis, OAM beams are a subset of structured light beams. Such structured beams are known to possess self-healing properties when partially obstructed along their propagation axis, especially in their near fields, resulting in partial reconstruction of their structures at larger distances along their beam axis. Various theoretical rationales have been proposed to explain, model and experimentally verify the self-healing physical effects in structured optical beams, using various types of obstructions and experimental techniques. Based on these models, we hypothesize that any self-healing observed will be greater as the OAM order increases. Here we observe the self-healing effects for the first time in structured OAM radio beams, in terms of communication signals and channel parameters rather than beam structures. We capture the effects of partial near-field obstructions of OAM beams of different orders on the communications signals and provide a physical rationale to substantiate that the self-healing effect was observed to increase with the order of OAM, agreeing with our hypothesis.
Effects of Transceiver Jitter on the Performance of Optical Scattering Communication Systems
Authors: Zanqiu Shen, Jianshe Ma, Serge B. Provost, Ping Su
Date: 2024-02-02 00:00:00

In ultraviolet communications, the transceiver jitter effects have been ignored in previous studies, which can result in non-negligible performance degradation especially in vibration states or in mobile scenes. To address this issue, we model the relationship between the received power and transceiver jitter by making use of a moment-based density function approximation method. Based on this relationship, we incorporate the transceiver jitter effects in combination with Poisson distribution. The error rate results are obtained assuming on-off key modulation with optimal threshold based detection. We validate the error rate expressions by comparing the analytical results with Monte-Carlo simulation results. The results show that the transceiver jitter effects cause performance degradation especially in smaller transceiver elevation angles or in shorter distances, which are often adopted in short-range ultraviolet communications. The results also show that larger elevation angle cases have a better performance with respect to anti-jitter and may perform better compared to smaller elevation angle situations in the case of larger standard deviation of jitter. This work studies for the first time the transceiver jitter effects in ultraviolet communications and provides guidelines for experimental system design.
When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators
Authors: Tobias Rüttenauer, Ozan Aksoy
Date: 2024-02-15 00:00:00

The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for how and when to use TWFE and the new DiD estimators in applied research.
Spatial Data Analysis
Authors: Tobias Rüttenauer
Date: 2024-02-15 00:00:00
Tasks: Econometrics

This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where spatial relationships among data points can be of substantive interest or can significantly impact statistical analyses. The chapter begins by exploring the fundamental concepts of spatial dependence and spatial autocorrelation, and highlighting their implications for traditional econometric models. It then introduces a range of spatial econometric models, particularly spatial lag, spatial error, spatial lag of X, and spatial Durbin models, illustrating how these models accommodate spatial relationships and yield accurate and insightful results about the underlying spatial processes. The chapter provides an intuitive guide on how to interpret those different models. A practical example on London house prices demonstrates the application of spatial econometrics, emphasising its relevance in uncovering hidden spatial patterns, addressing endogeneity, and providing robust estimates in the presence of spatial dependence.
Identification with Posterior-Separable Information Costs
Authors: Martin Bustos
Date: 2024-02-15 00:00:00
Tasks: counterfactual

I provide a model of rational inattention with heterogeneity and prove it is observationally equivalent to a state-dependent stochastic choice model subject to attention costs. I demonstrate that additive separability of unobservable heterogeneity, together with an independence assumption, suffice for the empirical model to admit a representative agent. Using conditional probabilities, I show how to identify: how covariates affect the desirability of goods, (a measure of) welfare, factual changes in welfare, and bounds on counterfactual market shares.
Quantile Granger Causality in the Presence of Instability
Authors: Alexander Mayer, Dominik Wied, Victor Troster
Date: 2024-02-15 00:00:00

We propose a new framework for assessing Granger causality in quantiles in unstable environments, for a fixed quantile or over a continuum of quantile levels. Our proposed test statistics are consistent against fixed alternatives, they have nontrivial power against local alternatives, and they are pivotal in certain important special cases. In addition, we show the validity of a bootstrap procedure when asymptotic distributions depend on nuisance parameters. Monte Carlo simulations reveal that the proposed test statistics have correct empirical size and high power, even in absence of structural breaks. Moreover, a procedure providing additional insight into the timing of Granger causal regimes based on our new tests is proposed. Finally, an empirical application in energy economics highlights the applicability of our method as the new tests provide stronger evidence of Granger causality.
A game theoretic approach to lowering incentives to violate speed limits in Finland
Authors: Mika Sutela, Nino Lindström
Date: 2024-02-14 00:00:00

We expand on earlier research on the topic by discussing an infinitely repeated game model with a subgame perfect equilibrium strategy profile (SPE) as a solution concept that diminishes incentives to violate speed limits in a carrot and stick fashion. In attempts to construct an SPE strategy profile, the initial state is chosen such that the drivers are playing a mixed strategy whereas the police is not enforcing with certainty. We also postulate a short period version of the repeated game with generalized stage game payoffs. For this game, we construct a multistage strategy profile that is a Nash equilibrium but not an SPE. Some solution candidates are excluded by showing that they do not satisfy a one shot deviation property that is a necessary condition for an SPE profile in a repeated game of perfect information.