pwc-archive/papers-with-abstracts huggingface dataset:
Stabilization with Prescribed Instant via Lyapunov Method
Authors: Jiyuan Kuang, Yabin Gao, Yizhuo Sun, Jiahui Wang, Aohua Liu, Yue Zhao, Jianxing Liu
Date: 2023-02-22 00:00:00

This letter investigates the prescribed-instant stabilization problem for high-order integrator systems. In anothor word, the settling time under the presented controller is independent of the initial conditions and equals the prescribed time instant. The controller is designed with the concept of backstepping. A strict proof based on the Lyapunov method is presented to clamp the settling time to the prescribed time instant from both the left and right sides. This proof serves as an example to present a general framework to verify the designed stabilization property. It should be emphasized that the prescribed-time stability (PSTS) [1] can only prescribe the upper bound of the settling time and is different from this work. The detailed argumentation will be presented after a brief review of the existing important research.
On the Emulation of Synchronous Machine Dynamics by Converter-Interfaced Generators
Authors: Georgios Tzounas, Federico Milano
Date: 2023-02-22 00:00:00

This paper discusses the conditions that a device needs to satisfy to replicate the behavior of a conventional synchronous machine (SM) connected to a power network. The conditions pertain to the device's stored energy, time scale of response, oscillation damping, and behavior during short-circuits. Relevant remarks for devices that do/don't satisfy these conditions are discussed through an illustrative numerical example as well as through simulation results based on a modified version of the well-known WSCC 9-bus test system.
Robust Auto-landing Control of an agile Regional Jet Using Fuzzy Q-learning
Authors: Mohsen Zahmatkesh, Seyyed Ali Emami, Afshin Banazadeh, Paolo Castaldi
Date: 2023-02-21 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

A robust auto-landing problem of a Truss-braced Wing (TBW) regional jet aircraft with poor stability characteristics is presented in this study employing a Fuzzy Reinforcement Learning scheme. Reinforcement Learning (RL) has seen a recent surge in practical uses in control systems. In contrast to many studies implementing Deep Learning in RL algorithms to generate continuous actions, the methodology of this study is straightforward and avoids complex neural network architectures by applying Fuzzy rules. An innovative, agile civil aircraft is selected not only to meet future aviation community expectations but also to demonstrate the robustness of the suggested method. In order to create a multi-objective RL environment, a Six-degree-of-freedom (6-DoF) simulation is first developed. By transforming the auto-landing problem of the aircraft into a Markov Decision Process (MDP) formulation, the problem is solved by designing a low-level Fuzzy Q-learning (FQL) controller. More specifically, the well-known Q-learning method, which is a discrete RL algorithm, is supplemented by Fuzzy rules to provide continuous actions with no need to complex learning structures. The performance of the proposed system is then evaluated by extensive flight simulations in different flight conditions considering severe wind gusts, measurement noises, actuator faults, and model uncertainties. Besides, the controller effectiveness would be compared with existing competing techniques such as Dynamic Inversion (DI) and Q-learning. The simulation results indicate the superior performance of the proposed control system as a reliable and robust control method to be employed in real applications.
Vandermonde Trajectory Bounds for Linear Companion Systems
Authors: Ömür Arslan, Aykut İşleyen
Date: 2023-02-21 00:00:00
Tasks: Motion Planning, motion prediction

Fast and accurate safety assessment and collision checking are essential for motion planning and control of highly dynamic autonomous robotic systems. Informative, intuitive, and explicit motion trajectory bounds enable explainable and time-critical safety verification of autonomous robot motion. In this paper, we consider feedback linearization of nonlinear systems in the form of proportional-and-higher-order-derivative (PhD) control corresponding to companion dynamics. We introduce a novel analytic convex trajectory bound, called $\textit{Vandermonde simplex}$, for high-order companion systems, that is given by the convex hull of a finite weighted combination of system position, velocity, and other relevant higher-order state variables. Our construction of Vandermonde simplexes is built based on expressing the solution trajectory of companion dynamics in a newly introduced family of $\textit{Vandermonde basis functions}$ that offer new insights for understanding companion system motion compared to the classical exponential basis functions. In numerical simulations, we demonstrate that Vandermonde simplexes offer significantly more accurate motion prediction (e.g., at least an order of magnitude improvement in estimated motion volume) for describing the motion trajectory of companion systems compared to the standard invariant Lyapunov ellipsoids as well as exponential simplexes built based on exponential basis functions.
Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit
Authors: Brooke E. Mason, Jacquelyn Schmidt, Branko Kerkez
Date: 2023-02-21 00:00:00
Tasks: Time Series, Time Series Analysis

The impact of green infrastructure (GI) on the urban drainage landscape remains largely unmeasured at high temporal and spatial scales. To that end, a data toolchain is introduced, underpinned by a novel wireless sensor network for continuously measuring real-time water levels in GI. The internet-connected sensors enable the collection of high-resolution data across large regions. A case study in Detroit (MI, US) is presented, where the water levels of 14 GI sites were measured in-situ from June to September 2021. The large dataset is analyzed using an automated storm segmentation methodology, which automatically extracts and analyzes individual storms from measurement time series. Storms are used to parameterize a dynamical system model of GI drawdown dynamics. The model is completely described by the decay constant {\alpha}, which is directly proportional to the drawdown rate. The parameter is analyzed across storms to compare GI dynamics between sites and to determine the major design and physiographic features that drive drawdown dynamics. A correlation analysis using Spearman's rank correlation coefficient reveals that depth to groundwater, imperviousness, longitude, and drainage area to surface area ratio are the most important features explaining GI drawdown dynamics in Detroit. A discussion is provided to contextualize these finding and explore the implications of data-driven strategies for GI design and placement.
Estimating the loss of economic predictability from aggregating firm-level production networks
Authors: Christian Diem, András Borsos, Tobias Reisch, János Kertész, Stefan Thurner
Date: 2023-02-22 00:00:00

To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.
Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes
Authors: Federico A. Bugni, Ivan A. Canay, Steve McBride
Date: 2023-02-22 00:00:00
Tasks: regression

This paper studies settings where the analyst is interested in identifying and estimating the average causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immediately realized after the treatment assignment, a feature that is ubiquitous in empirical settings. The period between the treatment and the realization of the outcome allows other observed actions to occur and affect the outcome. In this context, we study several regression-based estimands routinely used in empirical work to capture the average treatment effect and shed light on interpreting them in terms of ceteris paribus effects, indirect causal effects, and selection terms. We obtain three main and related takeaways. First, the three most popular estimands do not generally satisfy what we call \emph{strong sign preservation}, in the sense that these estimands may be negative even when the treatment positively affects the outcome conditional on any possible combination of other actions. Second, the most popular regression that includes the other actions as controls satisfies strong sign preservation \emph{if and only if} these actions are mutually exclusive binary variables. Finally, we show that a linear regression that fully stratifies the other actions leads to estimands that satisfy strong sign preservation.
Industrial Policy for Advanced AI: Compute Pricing and the Safety Tax
Authors: Mckay Jensen, Nicholas Emery-Xu, Robert Trager
Date: 2023-02-22 00:00:00

Using a model in which agents compete to develop a potentially dangerous new technology (AI), we study how changes in the pricing of factors of production (computational resources) affect agents' strategies, particularly their spending on safety meant to reduce the danger from the new technology. In the model, agents split spending between safety and performance, with safety determining the probability of a ``disaster" outcome, and performance determining the agents' competitiveness relative to their peers. For given parameterizations, we determine the theoretically optimal spending strategies by numerically computing Nash equilibria. Using this approach we find that (1) in symmetric scenarios, compute price increases are safety-promoting if and only if the production of performance scales faster than the production of safety; (2) the probability of a disaster can be made arbitrarily low by providing a sufficiently large subsidy to a single agent; (3) when agents differ in productivity, providing a subsidy to the more productive agent is often better for aggregate safety than providing the same subsidy to other agent(s) (with some qualifications, which we discuss); (4) when one agent is much more safety-conscious, in the sense of believing that safety is more difficult to achieve, relative to his competitors, subsidizing that agent is typically better for aggregate safety than subsidizing its competitors; however, subsidizing an agent that is only somewhat more safety-conscious often decreases safety. Thus, although subsidizing a much more safety-conscious, or productive, agent often improves safety as intuition suggests, subsidizing a somewhat more safety-conscious or productive agent can often be harmful.
Institutional reforms and the employment effects of spatially targeted investment grants: The case of Germany's GRW
Authors: Björn Alecke, Timo Mitze
Date: 2023-02-22 00:00:00

Spatially targeted investment grant schemes are a common tool to support firms in lagging regions. We exploit exogenous variations in Germany's main regional policy instrument (GRW) arriving from institutional reforms to analyse local employment effects of investment grants. Findings for reduced-form and IV regressions point to a significant policy channel running from higher funding rates to increased firm-level investments and newly created jobs. When we contrast effects for regions with high but declining funding rates to those with low but rising rates, we find that GRW reforms led to diminishing employment increases. Especially small firms responded to changing funding conditions.
Simple Analytics of the Government Investment Multiplier
Authors: Chunbing Cai, Jordan Roulleau-Pasdeloup
Date: 2023-02-22 00:00:00

What are the effects of investing in public infrastructure? We answer this question with a New Keynesian model. We recast the model as a Markov chain and develop a general solution method that nests existing ones inside/outside the zero lower bound as special cases. Our framework delivers a simple expression for the contribution of public infrastructure. We show that it provides a unified framework to study the effects of public investment in three scenarios: $(i)$ normal times $(ii)$ short-lived liquidity trap $(iii)$ long-lived liquidity trap. We find that calibrations commonly used lead to multipliers that diverge with the duration of the trap.
Enhancing Energy System Models Using Better Load Forecasts
Authors: Thomas Möbius, Mira Watermeyer, Oliver Grothe, Felix Müsgens
Date: 2023-02-21 00:00:00
Tasks: Time Series, Time Series Analysis

Energy system models require a large amount of technical and economic data, the quality of which significantly influences the reliability of the results. Some of the variables on the important data source ENTSO-E transparency platform, such as transmission system operators' day-ahead load forecasts, are known to be biased. These biases and high errors affect the quality of energy system models. We propose a simple time series model that does not require any input variables other than the load forecast history to significantly improve the transmission system operators' load forecast data on the ENTSO-E transparency platform in real-time, i.e., we successively improve each incoming data point. We further present an energy system model developed specifically for the short-term day-ahead market. We show that the improved load data as inputs reduce pricing errors of the model, with strong reductions particularly in times when prices are high and the market is tight.
Ignorance Is Bliss: The Screening Effect of (Noisy) Information
Authors: Felix Zhiyu Feng, Wenyu Wang, Yufeng Wu, Gaoqing Zhang
Date: 2023-02-22 00:00:00

This paper studies the value of a firm's internal information when the firm faces an adverse selection problem arising from unobservable managerial abilities. While more precise information allows the firm to make ex post more efficient investment decisions, noisier information has an ex ante screening effect that allows the firm to attract on-average better managers. The trade-off between more effective screening of managers and more informed investment implies a non-monotonic relationship between firm value and information quality. A marginal improvement in information quality does not necessarily lead to an overall improvement in firm value.
Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification
Authors: Mehmet Parlak
Date: 2023-02-22 00:00:00
Tasks: Classification, Deep Learning

Time-frequency images (TFIs) provide a joint time-frequency representation of a signal and have become an effective tool for analyzing, characterizing, and processing non-stationary signals. Deep learning (DL) techniques have become versatile for signal classification, enabling the automatic extraction of relevant features from raw data. In this paper, we present two use cases on the time-frequency transformation and deep learning techniques for signal classification, where signals are first pre-processed and transformed into TFIs, and their features are then extracted through deep learning neural networks and classification algorithms. The specific methods and algorithms used may vary depending on the particular application, therefore different methods for creating TFIs; the Short-Time Fourier Transform (STFT), Fourier-based Synchrosqueezing Transform (FSST), Wigner Ville distribution (WVD), Smoothed Pseudo-Wigner distribution (SPWD), Choi-Williams distribution (CWD), and Continuous Wavelet Transform (CWT) are investigated. The performance of various deep learning, and convolutional neural network (CNN) models such as ResNet-50, ShuffleNet, and Squeezenet are evaluated for their accuracy of classification in different applications and the results are compared with the results of the conventional machine learning and ensemble methods such as Multilayer Perceptrons (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and XGboost. The results of this research demonstrate that significant improvements in signal classification accuracy can be achieved by leveraging the combined power of TFIs, and deep learning models. These advances have found practical applications in a wide range of fields, including radar signal classification, stability analysis of power systems, speech and music recognition, and biomedical signal characterization.
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
Authors: Ce Ju, Reinmar Josef Kobler, Cuntai Guan
Date: 2023-02-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Frechet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.
RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design
Authors: Honghao Luo, Rang Liu, Ming Li, Qian Liu
Date: 2023-02-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been envisioned as a promising technique to alleviate the spectrum congestion problem. Inspired by the applications of reconfigurable intelligent surface (RIS) in dynamically manipulating wireless propagation environment, in this paper, we investigate to deploy a RIS in an ISAC system to pursue performance improvement. Particularly, we consider a RIS-assisted ISAC system where a multi-antenna base station (BS) performs multi-target detection and multi-user communication with the assistance of a RIS. Our goal is maximizing the weighted summation of target detection signal-to-noise ratios (SNRs) by jointly optimizing the transmit beamforming and the RIS reflection coefficients, while satisfying the communication quality-of-service (QoS) requirement, the total transmit power budget, and the restriction of RIS phase-shift. An efficient alternating optimization algorithm combining the majorization-minimization (MM), penalty-based, and manifold optimization methods is developed to solve the resulting complicated non-convex optimization problem. Simulation results illustrate the advantages of deploying RIS in ISAC systems and the effectiveness of our proposed algorithm.
Probabilistic Simplex Component Analysis by Importance Sampling
Authors: Nerya Granot, Tzvi Diskin, Nicolas Dobigeon, Ami Wiesel
Date: 2023-02-22 00:00:00

In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex component analysis (PRISM). Previous solutions to tackle this challenging problem were based on geometrical approaches or computationally intensive variational methods. In contrast, we propose a conventional expectation maximization (EM) algorithm which embeds importance sampling. For this purpose, the proposal distribution is chosen as a simple surrogate distribution of the target posterior that is guaranteed to lie in the simplex. This distribution is based on the Gaussian linear minimum mean squared error (LMMSE) approximation which is accurate at high signal-to-noise ratio. Numerical experiments in different settings demonstrate the advantages of this adaptive surrogate over state-of-the-art methods.
Lattice All-Pass Filter based Precoder Adaptation for MIMO Wireless Channels
Authors: Parth Mehta, Agulla Surya Bharath, Kumar Appaiah, Rajbabu Velmurugan, Debasattam Pal
Date: 2023-02-22 00:00:00
Tasks: All

Modern 5G communication systems employ multiple-input multiple-output (MIMO) in conjunction with orthogonal frequency division multiplexing (OFDM) to enhance data rates, particularly for wideband millimetre wave (mmW) applications. Since these systems use a large number of subcarriers, feeding back the estimated precoder for even a subset of subcarriers from the receiver to the transmitter is prohibitive. Moreover, such frequency domain approaches also do not exploit the predominant line-of-sight component that is present in such channels to reduce feedback. In this work, we view the precoder in the time domain as a matrix all-pass filter, and model the discrete-time precoder filter using a matrix-lattice structure that aids in reducing the overall feedback while still maintaining the desired frequency-phase delay profile. This provides an efficient precoder representation across the subcarriers using fewer coefficients, and is amenable to tracking over time with much lower feedback than past approaches. Compared to frequency domain geodesic interpolation, Givens rotation based parameterisation, and the angle-delay domain approach that depends on approximate discrete-time representation, the proposed approach yields higher achievable rates with a much lower feedback burden. Via extensive simulations over mmW channel models, we confirm the effectiveness of our claims, and show that the proposed approach can reduce the feedback burden by up to 70%.