pwc-archive/papers-with-abstracts huggingface dataset:
Gain-Only Neural Operators for PDE Backstepping
Authors: Rafael Vazquez, Miroslav Krstic
Date: 2024-03-28 00:00:00
Tasks: Scheduling

In this work we advance the recently-introduced deep learning-powered approach to PDE backstepping control by proposing a method that approximates only the control gain function -- a function of one variable -- instead of the entire kernel function of the backstepping transformation, which depends on two variables. This idea is introduced using several benchmark unstable PDEs, including hyperbolic and parabolic types, and extended to 2X2 hyperbolic systems. By employing a backstepping transformation that utilizes the exact kernel (suitable for gain scheduling) rather than an approximated one (suitable for adaptive control), we alter the quantification of the approximation error. This leads to a significant simplification in the target system, shifting the perturbation due to approximation from the domain to the boundary condition. Despite the notable differences in the Lyapunov analysis, we are able to retain stability guarantees with this simplified approximation approach. Approximating only the control gain function simplifies the operator being approximated and the training of its neural approximation, potentially reducing the neural network size. The trade-off for these simplifications is a more intricate Lyapunov analysis, involving higher Sobolev spaces for some PDEs, and certain restrictions on initial conditions arising from these spaces. It is crucial to carefully consider the specific requirements and constraints of each problem to determine the most suitable approach; indeed, recent works have demonstrated successful applications of both full-kernel and gain-only approaches in adaptive control and gain scheduling contexts.
Design and Evaluation of a DC Microgrid Testbed for DER Integration and Power Management
Authors: Gokul Krishnan S, Charalambos Konstantinou
Date: 2024-03-28 00:00:00
Tasks: Management

This paper presents a DC microgrid testbed setup that consists of various Distributed Energy Resources (DERs) including solar Photovoltaics (PV), supercapacitors for voltage regulation, and Battery Energy Storage Systems (BESS). The DC microgrid accommodates both non-flexible and flexible loads which can be dynamically adjusted based on PV power availability. The integration of the setup with the Hyphae Autonomous Power Interchange System (APIS) framework automates energy transfer within the BESS, ensuring efficient power management and optimizing the overall efficiency of the DC microgrid. Furthermore, the setup is validated in terms of the efficacy of the proposed model via real-time simulation, facilitated by the Speedgoat baseline real-time target Hardware-in-the-Loop (HIL) machine. The results demonstrate the model's adeptness in efficiently managing power sharing, emphasizing the capabilities of the DC microgrid setup in terms of performance and reliability in dynamic energy scenarios as well as enhancing the resilience of the grid amidst PV uncertainties.
Tunable Superconducting Magnetic Levitation with Self-Stability
Authors: Qi Xu, Yi Lin, Yunfei Tan, Jianzhao Geng
Date: 2024-03-28 00:00:00

Magnetic levitation based on the flux pinning nature of type II superconductors has the merit of self-stability, making it appealing for applications such as high speed bearings, maglev trains, space generators, etc. However, such levitation systems physically rely on the superconductor pre-capturing magnetic flux (i.e. field cooling process) before establishing the levitation state which is nonadjustable afterwards. Moreover, practical type II superconductors in the levitation system inevitably suffer from various sources of energy losses, leading to continuous levitation force decay. These intrinsic drawbacks make superconducting maglev inflexible and impractical for long term operation. Here we propose and demonstrate a new form of superconducting maglev which is tunable and with self-stability. The maglev system uses a closed-loop type II superconducting coil to lock flux of a magnet, establishing self-stable levitation between the two objects. A flux pump is used to modulate the total magnetic flux of the coil without breaking its superconductivity, thus flexibly tuning levitation force and height meanwhile maintaining self-stability. For the first time, we experimentally demonstrate a self-stable type II superconducting maglev system which is able to: counteract long term levitation force decay, adjust levitation force and equilibrium position, and establish levitation under zero field cooling condition. These breakthroughs may bridge the gap between demonstrations and practical applications of type II superconducting maglevs.
Resilience-Oriented Operation of Micro-Grids in both Grid-Connected and Isolated Conditions within Sustainable Active Distribution Networks
Authors: Saeed Behzadi, Amir Bagheri, Abbas Rabiee
Date: 2024-03-28 00:00:00

Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). In this paper, a new convex optimization model is proposed with two objective functions including energy loss reduction in normal operating mode and system load shedding minimization in critical conditions after the occurrence of natural disasters. This purpose is fulfilled through optimal allocation of distributed generation (DG) units from both conventional and renewable types as well as energy storage systems (ESSs). In addition, a new formulation has been derived to form optimal micro-grids (MGs) aiming at energy loss reduction in normal operating condition and resiliency index improvement under emergency situations. The developed model is implemented in GAMS software and the studies have been tested and analyzed on the IEEE 33-bus system. The results verify the effectiveness of the proposed method in terms of energy loss reduction as well as resilience enhancement in extreme operation condition following severe disruptions in the system.
Symbiotic Control of Uncertain Dynamical Systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures
Authors: Tansel Yucelen, Selahattin Burak Sarsilmaz, Emre Yildirim
Date: 2024-03-28 00:00:00

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters).
Harnessing Data for Accelerating Model Predictive Control by Constraint Removal
Authors: Zhinan Hou, Feiran Zhao, Keyou You
Date: 2024-03-28 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints. To accelerate online computation of MPC, we utilize data to adaptively remove the constraints while maintaining the MPC policy unchanged. Specifically, we design the removal rule based on the Lipschitz continuity of the MPC policy. This removal rule can use the information of historical data according to the Lipschitz constant and the distance between the current state and historical states. In particular, we provide the explicit expression for calculating the Lipschitz constant by the model parameters. Finally, simulations are performed to validate the effectiveness of the proposed method.
Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels
Authors: Daniel Menges, Trym Tengesdal, Adil Rasheed
Date: 2024-03-27 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on Nonlinear Model Predictive Control (NMPC). Artificial Potential Fields (APFs) set the foundation for the cost function of the optimal control problem in terms of collision avoidance and anti-grounding. Depending on the risk of a collision given by the resulting force of the APFs, the controller optimizes regarding an adapted heading and travel speed by additionally following a desired path. For this purpose, nonlinear vessel dynamics are used for the NMPC. To extend the situational awareness concerning environmental disturbances impacted by wind, waves, and sea currents, a nonlinear disturbance observer is coupled to the entire NMPC scheme, allowing for the correction of an incorrect vessel motion due to external forces. In addition, the most essential rules according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are considered. The results of the simulations show that the proposed framework can control an autonomous surface vessel under various challenging scenarios, including environmental disturbances, to avoid collisions and follow desired paths.
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Authors: Ruyi Yang, Jingyu Hu, Zihao Li, Jianli Mu, Tingzhao Yu, Jiangjiang Xia, Xuhong LI, Aritra Dasgupta, Haoyi Xiong
Date: 2024-03-24 00:00:00
Tasks: Interpretable Machine Learning

Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: 1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. 2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.
Transmission IR Microscopy for the Quantitation of Biomolecular Mass In Live Cells
Authors: Yow-Ren Chang, Seong-Min Kim, Young Jong Lee
Date: 2024-03-27 00:00:00

Absolute quantity imaging of biomolecules on a single cell level is critical for measurement assurance in biosciences and bioindustries. While infrared (IR) transmission microscopy is a powerful label-free imaging modality capable of chemical quantification, its applicability to hydrated biological samples remains challenging due to the strong water absorption. We overcome this challenge by applying a solvent absorption compensation (SAC) technique to a home-built quantum cascade laser IR microscope. SAC-IR microscopy improves the chemical sensitivity considerably by adjusting the incident light intensity to pre-compensate the IR absorption by water while retaining the full dynamic range. We demonstrate the label-free chemical imaging of key biomolecules of a cell, such as protein, fatty acid, and nucleic acid, with sub-cellular spatial resolution. By imaging live fibroblast cells over twelve hours, we monitor the mass change of the three molecular species of single cells at various phases, including cell division. While the current live-cell imaging demonstration involved three wavenumbers, more wavenumber images could measure more biomolecules in live cells with higher accuracy. As a label-free method to measure absolute quantities of various molecules in a cell, SAC-IR microscopy can potentially become a standard chemical characterization tool for live cells in biology, medicine, and biotechnology.
Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data
Authors: Isabell Stucke, Deborah Morgenstern, Georg J. Mayr, Thorsten Simon, Achim Zeileis, Gerhard Diendorfer, Wolfgang Schulz, Hannes Pichler
Date: 2024-03-18 00:00:00

This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and $35$ larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 meters and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 km$^2$ resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects.
On the potential of quantum walks for modeling financial return distributions
Authors: Stijn De Backer, Luis E. C. Rocha, Jan Ryckebusch, Koen Schoors
Date: 2024-03-28 00:00:00

Accurate modeling of the temporal evolution of asset prices is crucial for understanding financial markets. We explore the potential of discrete-time quantum walks to model the evolution of asset prices. Return distributions obtained from a model based on the quantum walk algorithm are compared with those obtained from classical methodologies. We focus on specific limitations of the classical models, and illustrate that the quantum walk model possesses great flexibility in overcoming these. This includes the potential to generate asymmetric return distributions with complex market tendencies and higher probabilities for extreme events than in some of the classical models. Furthermore, the temporal evolution in the quantum walk possesses the potential to provide asset price dynamics.
Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy
Authors: Muzi Chen, Yuhang Wang, Boyao Wu, Difang Huang
Date: 2024-03-28 00:00:00

The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the LASSO method to construct the stock network and further use the Map Equation method to analyze the evolution of modules in the SSE A-shares market. Empirical results show: (1) The connected effect is more significant in bear markets than bull markets; (2) A system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) Some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies.
Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price
Authors: Muzi Chen, Nan Li, Lifen Zheng, Difang Huang, Boyao Wu
Date: 2024-03-28 00:00:00

The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the maximum strongly connected subgraph, and $3\sigma$ principle to iteratively determine the threshold value for building a dynamic correlation network of SSE A-shares. Analyses are carried out based on the networking structure, intra-sector connectivity, and node status, identifying several contributions. First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015. Second, the finance, energy and utilities sectors have a stronger intra-industry connectivity than other sectors. Third, HUB nodes drive the growth of the SSE A-shares market during bull periods, while stocks have a think-tail degree distribution in bear periods and show distinct characteristics in terms of market value and finance. Granger linear and non-linear causality networks are also considered for the comparison purpose. Studies on the evolution of inter-cycle connectivity in the SSE A-share market may help investors improve portfolios and develop more robust risk management policies.
On Causal Inference with Model-Based Outcomes
Authors: Dmitry Arkhangelsky, Kazuharu Yanagimoto, Tom Zohar
Date: 2024-03-28 00:00:00
Tasks: Causal Inference, model, regression

We study a causal inference problem with group-level outcomes, which are themselves parameters identified from microdata. We formalize these outcomes using population moment conditions and demonstrate that one-step Generalized Method of Moments (GMM) estimators are generally inconsistent due to an endogenous weighting bias, where policy affects the implicit GMM weights. In contrast, two-stage Minimum Distance (MD) estimators perform well when group sizes are sufficiently large. While MD estimators can still be inconsistent in small groups due to a policy-induced sample selection, we demonstrate that this can be addressed by incorporating auxiliary population information. An empirical application illustrates the practical importance of these findings.
Blockchains, MEV and the knapsack problem: a primer
Authors: Vijay Mohan, Peyman Khezr
Date: 2024-03-28 00:00:00

In this paper, we take a close look at a problem labeled maximal extractable value (MEV), which arises in a blockchain due to the ability of a block producer to manipulate the order of transactions within a block. Indeed, blockchains such as Ethereum have spent considerable resources addressing this issue and have redesigned the block production process to account for MEV. This paper provides an overview of the MEV problem and tracks how Ethereum has adapted to its presence. A vital aspect of the block building exercise is that it is a variant of the knapsack problem. Consequently, this paper highlights the role of designing auctions to fill a knapsack--or knapsack auctions--in alleviating the MEV problem. Overall, this paper presents a survey of the main issues and an accessible primer for researchers and students wishing to explore the economics of block building and MEV further.
Towards Standardized Regulations for Block Chain Smart Contracts: Insights from Delphi and SWARA Analysis
Authors: Shahin Heidari, Shannon Hashemi, Mohammad-Soroush Khorsand, Alireza Daneshfar, Seyedalireza Jazayerifar
Date: 2024-03-27 00:00:00

The rise of digital currency and the public ledger Block Chain has led to the development of a new type of electronic contract known as "smart contracts." For these contracts to be considered valid, they must adhere to traditional contract rules and be concluded without any impediments. Once written, encrypted, and signed, smart contracts are recorded in the Block Chain Ledger, providing transparent and secure record-keeping. Smart contracts offer several benefits, including their ability to execute automatically without requiring human intervention, their provision of public visibility of contract provisions on the Block Chain, their avoidance of financial crimes like Money Laundering, and their prevention of contract abuses. However, disputes arising from smart contracts still require human intervention, presenting unique challenges in enforcing these contracts, such as evidentiary issues, enforceability of waivers of defenses, and jurisdictional and choice-of-law considerations. Due to the novel nature of smart contracts, there are currently no standardized regulations that apply to them. Countries that have approved them have turned to customary law to legitimize their use. The Delphi method was used to identify critical success factors for applying blockchain transactions in a manufacturing company. Stepwise Weight Assessment Ratio Analysis (SWARA) was then utilized to determine the most influential factors. The proposed methodology was implemented, and results show that the most influential factors for the successful application of blockchain transactions as smart contracts in a manufacturing company are: turnover, the counter argument, vision, components for building, and system outcome quality. Conversely, connections with government entities and subcontractors, and the guarantee of quality have the least influence on successful implementation.