pwc-archive/papers-with-abstracts huggingface dataset:
Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach
Authors: inghao Cao, Subhan Khan, Wanchun Liu, Yonghui Li, Branka Vucetic
Date: 2024-03-13 00:00:00
Tasks: Model Predictive Control

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein to enhance the realism of the underlying assumption in a real-world context. To achieve a desirable performance, the proposed control scheme leverages the predictive capabilities of a direct multiple shooting MPC, employs a compensation strategy to mitigate the impact of wireless channel imperfections. Instead of feeding noisy measurements into the MPC, we employ an Extended Kalman Filter (EKF) to mitigate the influence of measurement noise and process disturbances. Finally, we implement the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conduct a series of experiments to evaluate the performance of our control scheme across various scenarios. Through our simulation results and comparative analyses, we have substantiated the effectiveness and improvements brought about by our approach through the utilization of multiple metrics.
Prototyping and Experimental Results for Environment-Aware Millimeter Wave Beam Alignment via Channel Knowledge Map
Authors: Zhuoyin Dai, Di wu, Zhenjun Dong, Kun Li, Dingyang Ding, Sihan Wang, Yong Zeng
Date: 2024-03-13 00:00:00

Channel knowledge map (CKM), which aims to directly reflect the intrinsic channel properties of the local wireless environment, is a novel technique for achieving environmentaware communication. In this paper, to alleviate the large training overhead in millimeter wave (mmWave) beam alignment, an environment-aware and training-free beam alignment prototype is established based on a typical CKM, termed beam index map (BIM). To this end, a general CKM construction method is first presented, and an indoor BIM is constructed offline to learn the candidate transmit and receive beam index pairs for each grid in the experimental area. Furthermore, based on the location information of the receiver (or the dynamic obstacles) from the ultra-wide band (UWB) positioning system, the established BIM is used to achieve training-free beam alignment by directly providing the beam indexes for the transmitter and receiver. Three typical scenarios are considered in the experiment, including quasi-static environment with line-of-sight (LoS) link, quasistatic environment without LoS link and dynamic environment. Besides, the receiver orientation measured from the gyroscope is also used to help CKM predict more accurate beam indexes. The experiment results show that compared with the benchmark location-based beam alignment strategy, the CKM-based beam alignment strategy can achieve much higher received power, which is close to that achieved by exhaustive beam search, but with significantly reduced training overhead.
Guaranteeing Service in Connected Microgrids: Storage Planning and Optimal Power Sharing Policy
Authors: Arnab Dey, Vivek Khatana, Ankur Mani, Murti V. Salapaka
Date: 2024-03-12 00:00:00

The integration of renewable energy sources (RES) into power distribution grids poses challenges to system reliability due to the inherent uncertainty in their power production. To address this issue, battery energy sources (BESs) are being increasingly used as a promising solution to counter the uncertainty associated with RES power production. During the overall system planning stage, the optimal capacity of the BES has to be decided. In the operational phase, policies on when to charge the BESs and when to use them to support loads must be determined so that the BES remains within its operating range, avoiding depletion of charge on one hand and remaining within acceptable margins of maximum charge on the other. In this paper, a stochastic control framework is used to determine battery capacity, for microgrids, which ensures that during the operational phase, BESs' operating range is respected with pre-specified high probability. We provide an explicit analytical expression of the required BESs energy capacity for a single microgrid with RES as the main power source. Leveraging insights from the single microgrid case, the article focuses on the design and planning of BESs for the two-microgrid scenario. In this setting, microgrids are allowed to share power while respecting the capacity constraints imposed by the power lines. We characterize the optimal power transfer policy between the microgrids and the optimal BES capacity for multiple microgrids. This provides the BES savings arising from connecting the microgrids.
Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future
Authors: Mohd. Tasleem Khan
Date: 2024-03-12 00:00:00
Tasks: Computational Efficiency

The utilization of distributed arithmetic (DA) in AF algorithms has gained significant attention in recent years due to its potential to enhance computational efficiency and reduce resource requirements. This paper presents an exploration of the application of DA to adaptive filtering (AF) algorithms, analyzing trends, discussing challenges, and outlining future prospects. It begins by providing an overview of both DA and AF algorithms, highlighting their individual merits and established applications. Subsequently, the integration of DA into AF algorithms is explored, showcasing its ability to optimize multiply-accumulate operations and mitigate the computational burden associated with AF algorithms. Throughout the paper, the critical trends observed in the field are discussed, including advancements in DA-based hardware architectures. Moreover, the challenges encountered in implementing DA-based AF is also discussed. The continued evolution of DA techniques to cater to the demands of modern AF applications, including real-time processing, resource-constrained environments, and high-dimensional data streams is anticipated. In conclusion, this paper consolidates the current state of applying DA to AF algorithms, offering insights into prevailing trends, discussing challenges, and presenting future research and development in the field. The fusion of these two domains holds promise for achieving improved computational efficiency, reduced hardware complexity, and enhanced performance in various signal processing applications.
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Authors: Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai, Alexandre Capone, Velimir Todorovski, Stefan Sosnowski, Sandra Hirche
Date: 2024-03-12 00:00:00

In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with known dynamics, it remains largely unaddressed for systems with unknown dynamics. In this paper, we propose a Gaussian process-based time-varying control method that leverages backstepping and control barrier functions to achieve safety requirements within prescribed time windows for control affine systems. It can be used to keep a system within a safe region or to make it return to a safe region within a limited time window. These properties are cemented by rigorous theoretical results. The effectiveness of the proposed controller is demonstrated in a simulation of a robotic manipulator.
Configuration and EMT Simulation of the 240-bus MiniWECC System Integrating Offshore Wind Farms (OWFs)
Authors: Buxin She, Hisham Mahmood, Marcelo Elizondo, Veronica Adetola, Yuqing Dong
Date: 2024-03-12 00:00:00

As offshore wind farms (OWFs) become increasingly prevalent in Northern California and Southern Oregon, they introduce faster dynamics into the Western Electricity Coordinating Council (WECC) system, reshaping its dynamic behavior. Accordingly, electromagnetic transient (EMT) simulation is essential to assess high frequency dynamics of the WECC system with integrated OWFs. Against this background, this paper presents the integration of detailed dynamic models of OWFs into a 240-bus miniWECC system in PSCAD software. The sequential initialization technique is employed to facilitate the smooth initiation of a large-scale system in an EMT simulation. The performance of the configured model is assessed under wind speed variations and grounded faults, demonstrating the effectiveness of the miniWECC system with OWFs. This system serves as a valuable basic use case for validating the fast dynamic performance of future WECC systems with high penetration of wind energy.
Invalid proxies and volatility changes
Authors: Giovanni Angelini, Luca Fanelli, Luca Neri
Date: 2024-03-13 00:00:00

When in proxy-SVARs the covariance matrix of VAR disturbances is subject to exogenous, permanent breaks that cause IRFs to change across volatility regimes, even strong, exogenous external instruments yield inconsistent estimates of the dynamic causal effects. However, if these volatility shifts are properly incorporated into the analysis through (testable) "stability restrictions", we demonstrate that the target IRFs are point-identified and can be estimated consistently under a necessary and sufficient rank condition. If the shifts in volatility are sufficiently informative, standard asymptotic inference remains valid even with (i) local-to-zero covariance between the proxies and the instrumented structural shocks, and (ii) potential failures of instrument exogeneity. Intuitively, shifts in volatility act similarly to strong instruments that are correlated with both the target and non-target shocks. We illustrate the effectiveness of our approach by revisiting a seminal fiscal proxy-SVAR for the US economy. We detect a sharp change in the size of the tax multiplier when the narrative tax instrument is complemented with the decline in unconditional volatility observed during the transition from the Great Inflation to the Great Moderation. The narrative tax instrument contributes to identify the tax shock in both regimes, despite our empirical analysis raises concerns about its "statistical" validity.
Identifying Treatment and Spillover Effects Using Exposure Contrasts
Authors: Michael P. Leung
Date: 2024-03-13 00:00:00

To report spillover effects, a common practice is to regress outcomes on statistics capturing treatment variation among neighboring units. This paper studies the causal interpretation of nonparametric analogs of these estimands, which we refer to as exposure contrasts. We demonstrate that their signs can be inconsistent with those of the unit-level effects of interest even under unconfounded assignment. We then provide interpretable restrictions under which exposure contrasts are sign preserving and therefore have causal interpretations. We discuss the implications of our results for cluster-randomized trials, network experiments, and observational settings with peer effects in selection into treatment.
Imputation of Counterfactual Outcomes when the Errors are Predictable
Authors: Silvia Goncalves, Serena Ng
Date: 2024-03-12 00:00:00
Tasks: Causal Inference, counterfactual, Imputation, Time Series

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (\blup) of \citet{goldberger:62} in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed \pup\; is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals.
Complex economics of simple periodic systems
Authors: Petri P. Karenlampi
Date: 2024-03-13 00:00:00

This paper investigates the financial economics of simple periodic systems. Well-established financial procedures appear to be complicated, and lead to partially biased results. Probability theory is applied, and the focus is on the finances of simple periodic growth processes, in the absence of intermediate divestments. The expected value of the profit rate, derived from accounting measures on an accrual basis, does not depend on the capitalization path. The expected value of capitalization is path dependent. Because of the path-dependent capitalization, the return rate on capital is path-dependent, and the time-average return rate on capital differs from the expected value of the return rate on capital for the growth cycle. The internal rate of return, defined through a compounding equation, is path-independent, thereby differing from the expected value of the rate of return on capital. It is shown that within a production estate, the area-average of internal rate of return is not representative of the rate of return on capital. The growth cycle length maximizing the return rate on equity is independent of market interest rate. Leverage effect enters the microeconomics of the growth processes through a separate leverage equation, where the leverage coefficient may reach positive or negative values. The leverage effect on the internal rate of return and the net present value are discussed. Both effects are solvable, resulting in incorrect estimates.
A vicious cycle along busy bus corridors and how to abate it
Authors: Minyu Shen, Weihua Gu, Michael J. Cassidy, Yongjie Lin, Wei Ni
Date: 2024-03-13 00:00:00

We unveil that a previously-unreported vicious cycle can be created when bus queues form at curbside stops along a corridor. Buses caught in this cycle exhibit growing variation in headways as they travel from stop to stop. Bus (and patron) delays accumulate in like fashion and can grow large on long, busy corridors. We show that this damaging cycle can be abated in simple ways. Present solutions entail holding buses at a corridor entrance and releasing them as per various strategies proposed in the literature. We introduce a modest variant to the simplest of these strategies. It releases buses at headways that are slightly less than, or equal to, the scheduled values. It turns out that periodically releasing buses at slightly smaller headways can substantially reduce bus delays caused by holding so that benefits can more readily outweigh costs in corridors that contain a sufficient number of serial bus stops. The simple variant is shown to perform about as well as, or better than, other bus-holding strategies in terms of saving delays, and is more effective than other strategies in regularizing bus headways. We also show that grouping buses from across multiple lines and holding them by group can be effective when patrons have the flexibility to choose buses from across all lines in a group. Findings come by formulating select models of bus-corridor dynamics and using these to simulate part of the Bus Rapid Transit corridor in Guangzhou, China.
The social value of overreaction to information
Authors: Matteo Bizzarri, Daniele d'Arienzo
Date: 2024-03-13 00:00:00
Tasks: Diagnostic, Form

We study the welfare effects of overreaction to information in the form of diagnostic expectations in markets with asymmetric information, and the effect of a simple intervention in the form of a tax or a subsidy. A large enough level of overreaction is always welfare-decreasing and can rationalize a tax on financial transactions. A small degree of overreaction to private information can both increase or decrease welfare. This is because there are two competing externalities: an information externality, due to the informational role of prices, and a pecuniary externality, due to the allocative role of prices. When the information externality prevails on the pecuniary externality, the loading on private information in agents' trades is too small compared to the welfare optimum: in this case, a small degree of overreaction is welfare-improving.
Tournament Auctions
Authors: Luca Anderlini, GaOn Kim
Date: 2024-03-12 00:00:00

We examine ``tournament'' second-price auctions in which $N$ bidders compete for the right to participate in a second stage and contend against bidder $N+1$. When the first $N$ bidders are committed so that their bids cannot be changed in the second stage, the analysis yields some unexpected results. The first $N$ bidders consistently bid above their values in equilibrium. When bidder $N+1$ is sufficiently stronger than the first $N$, overbidding leads to an increase in expected revenue in comparison to the standard second-price auction when $N$ is large.
Score-based mechanisms
Authors: Eduardo Perez-Richet, Vasiliki Skreta
Date: 2024-03-12 00:00:00

We propose a mechanism design framework that incorporates both soft information, which can be freely manipulated, and semi-hard information, which entails a cost for falsification. The framework captures various contexts such as school choice, public housing, organ transplant and manipulations of classification algorithms. We first provide a canonical class of mechanisms for these settings. The key idea is to treat the submission of hard information as an observable and payoff-relevant action and the contractible part of the mechanism as a mapping from submitted scores to a distribution over decisions (a score-based decision rule). Each type report triggers a distribution over score submission requests and a distribution over decision rules. We provide conditions under which score-based mechanisms are without loss of generality. In other words, situations under which the agent does not make any type reports and decides without a mediator what score to submit in a score-based decision rule. We proceed to characterize optimal approval mechanisms in the presence of manipulable hard information. In several leading settings optimal mechanisms are score-based (and thus do not rely on soft information) and involve costly screening. The solution methodology we employ is suitable both for concave cost functions and quadratic costs and is applicable to a wide range of contexts in economics and in computer science.
Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
Authors: Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, Julia A. Schnabel
Date: 2024-03-13 00:00:00
Tasks: Quantitative MRI

We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://github.com/HannahEichhorn/PHIMO.
Hair and scalp disease detection using deep learning
Authors: Kavita Sultanpure, Bhairavi Shirsath, Bhakti Bhande, Harshada Sawai, Srushti Gawade, Suraj Samgir
Date: 2024-03-09 00:00:00
Tasks: Deep Learning, Medical Image Analysis

In recent years, there has been a notable advancement in the integration of healthcare and technology, particularly evident in the field of medical image analysis. This paper introduces a pioneering approach in dermatology, presenting a robust method for the detection of hair and scalp diseases using state-of-the-art deep learning techniques. Our methodology relies on Convolutional Neural Networks (CNNs), well-known for their efficacy in image recognition, to meticulously analyze images for various dermatological conditions affecting the hair and scalp. Our proposed system represents a significant advancement in dermatological diagnostics, offering a non-invasive and highly efficient means of early detection and diagnosis. By leveraging the capabilities of CNNs, our model holds the potential to revolutionize dermatology, providing accessible and timely healthcare solutions. Furthermore, the seamless integration of our trained model into a web-based platform developed with the Django framework ensures broad accessibility and usability, democratizing advanced medical diagnostics. The integration of machine learning algorithms into web applications marks a pivotal moment in healthcare delivery, promising empowerment for both healthcare providers and patients. Through the synergy between technology and healthcare, our paper outlines the meticulous methodology, technical intricacies, and promising future prospects of our system. With a steadfast commitment to advancing healthcare frontiers, our goal is to significantly contribute to leveraging technology for improved healthcare outcomes globally. This endeavor underscores the profound impact of technological innovation in shaping the future of healthcare delivery and patient care, highlighting the transformative potential of our approach.
A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems in Wireless Sensor Networks
Authors: Charles Hovine, Alexander Bertrand
Date: 2024-03-13 00:00:00

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion problems, we recently proposed the Distributed Adaptive Signal Fusion (DASF) algorithm, which distributively computes a spatial filter expressed as the solution of a smooth optimization problem involving the network-wide sensor signal statistics. In this work, we show that the DASF algorithm can be extended to compute the filters associated with a certain class of non-smooth optimization problems. This extension makes the addition of sparsity-inducing norms to the problem's cost function possible, allowing sensor selection to be performed in a distributed fashion, alongside the filtering task of interest, thereby further reducing the network's energy consumption. We provide a description of the algorithm, prove its convergence, and validate its performance and solution tracking capabilities with numerical experiments.