pwc-archive/papers-with-abstracts huggingface dataset:
Region of Attraction Estimation Using Union Theorem in Sum-of-Squares Optimization
Authors: Bhaskar Biswas, Dmitry Ignatyev, Argyrios Zolotas, Antonios Tsourdos
Date: 2023-05-19 00:00:00

Appropriate estimation of Region of Attraction for a nonlinear dynamical system plays a key role in system analysis and control design. Sum-of-Squares optimization is a powerful tool enabling Region of Attraction estimation for polynomial dynamical systems. Employment of a positive definite function called shape function within the Sum-of-Squares procedure helps to find a richer representation of the Lyapunov function and a larger corresponding Region of Attraction estimation. However, existing Sum-of-Squares optimization techniques demonstrate very conservative results. The main novelty of this paper is the Union theorem which enables the use of multiple shape functions to create a polynomial Lyapunov function encompassing all the areas generated by the shape functions. The main contribution of this paper is a novel computationally-efficient numerical method for Region of Attraction estimation, which remarkably improves estimation performance and overcomes limitations of existing methods, while maintaining the resultant Lyapunov function polynomial, thus facilitating control system design and construction of control Lyapunov function with enhanced Region of Attraction using conventional Sum-of-Squares tools. A mathematical proof of the Union theorem along with its application to the numerical algorithm of Region of Attraction estimation is provided. The method yields significantly enlarged Region of Attraction estimations even for systems with non-symmetric or unbounded Region of Attraction, which is demonstrated via simulations of several benchmark examples.
Enhancing data security against cyberattacks in artificial intelligence based smartgrid systems with crypto agility
Authors: Marcelo Simoes, Mohammed Elmusrati, Tero Vartiainen, Mike Mekkanen, Mazaher Karimi, Sayawu Diaba, Emmanuel Anti, Wilson Lopes
Date: 2023-05-19 00:00:00
Tasks: Decision Making, energy management, Management

A new paradigm of electricity generation at the distribution level, with renewable and alternative sources, is possible with microgrids. The main idea is to have microgrids deployed on low- or medium-voltage active distribution networks. They can be advantageous in many different ways, such as improving the energy efficiency and reliability of the system and reducing transmission losses and network congestion. There are challenges in implementing MGs with DER units, those are related to power quality and stability issues voltage and fault level changes, energy management, low inertia, further complex protection schemes, load and generation forecasting, cyber-attacks, and cyber security. This paper shows the deep utilization of advanced, accurate, and fast methodologies such as artificial intelligence-based techniques. They guarantee efficient, optimal, safe, and reliable operation of smart grids safe against cyberattacks. AI refers to the computer-based system's ability to perform tasks with intelligence typically associated with human decision-making, they can learn from past experiences and solve problems.
A Real-Time Limit Order Book as a Market Mechanism for Transactive Energy Systems
Authors: Akshay Sreekumar, Adhithyan Sakthivelu, Rimvydas Baltaduonis, Lynne Kiesling, Seth Hoedl, David P. Chassin
Date: 2023-05-19 00:00:00

This paper presents a limit order book (LOB) market mechanism design for transactive energy systems. The proposed design is planned for deployment in New Hampshire and Maine under a US Department of Energy Connected Communities project. The new LOB mechanism is intended to replace or work in conjunction with the conventional transactive energy double auction mechanisms designed for retail real-time electricity price discovery, and will facilitate significant scaling of transactive energy systems. The paper provides LOB market rules, clearing algorithm, and illustrative examples and discusses clearing algorithm performance and reliability. The proposed LOB design includes support for discovering prices arising from wholesale electricity markets, distribution system asset constraints, distributed energy resource constraints, and consumer willingness to consume or produce at a reservation price.
Coordinated Frequency-Constrained Stochastic Economic Dispatch for Integrated Transmission and Distribution System via Distributed Optimization
Authors: Ye Tian, Zhengshuo Li
Date: 2023-05-19 00:00:00
Tasks: Distributed Optimization

When large-scale uncertain centralized and distributed renewable energy sources are connected to a power system, separate dispatching of the transmission power system (TPS) and the active distribution network (ADN) will lower the network security and frequency security of the system. To address these problems, this paper proposes a coordinated frequency-constrained stochastic economic dispatch (CFC-SED) model for an integrated transmission and distribution (ITD) system. In this model, the dynamic frequency security constraints and network security constraints of the ITD system are constructed, and the joint chance constraints are adopted to handle the uncertainty. Then, the control parameters of inverter-based resources, the base point power, and the regulation reserve of all dispatchable resources in the ITD system are jointly optimized for the minimum operating cost. TPS and ADNs can deliver base point power bidirectionally and provide frequency regulation support bidirectionally, which extend the existing reserve assumption in ITD dispatch and enhance the operational security of the ITD system. Moreover, based on the alternating direction of multiplier algorithm, a two-layer distributed optimization framework is proposed to solve the CFC-SED model. Case studies show that the CFC-SED model can fully utilize the potential of multiple regulation resources to improve the security performance of the ITD system, and TPS and ADNs can be coordinated efficiently through the proposed distributed optimization framework.
Robust MPC with Zone Tracking
Authors: Zhiyinan Huang, Jinfeng Liu, Biao Huang
Date: 2023-05-19 00:00:00
Tasks: Model Predictive Control

We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work. The proposed ZMPC has guaranteed convergence into the target zone in the presence of bounded disturbance. The proposed approach achieves this by modifying the actual target zone such that the effect of disturbances is rejected. A control invariant set (CIS) inside the modified target zone is used as the terminal set, which ensures the closed-loop stability of the proposed controller. Detailed closed-loop stability analysis is presented. Simulation studies based on a continuous stirred tank reactor (CSTR) are performed to validate the effectiveness of the proposed ZMPC.
A Cyberattack Detection-Isolation Scheme For CAV Under Changing Driving Environment
Authors: Sanchita Ghosh, Nutan Saha, Tanushree Roy
Date: 2023-05-18 00:00:00

Under a changing driving environment, a Connected Autonomous Vehicle (CAV) platoon relies strongly on the acquisition of accurate traffic information from neighboring vehicles as well as reliable commands from a centralized supervisory controller through the communication network. Even though such modalities are imperative to ensure the safe and efficient driving performance of CAVs, they led to multiple security challenges. Thus, a cyberattack on this network can corrupt vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which can lead to unsafe or undesired driving scenarios. Hence, in this paper, we propose a cyberattack detection-isolation algorithm comprised of a unified V2V and V2I cyberattack detection scheme along with a V2I isolation scheme for CAVs under changing driving conditions. The proposed algorithm is constructed using a bank of residual generators with Lyapunov function-based performance guarantees, such as disturbance-to-state stability, robustness, and sensitivity. Finally, we showcase the efficacy of our proposed algorithm through extensive Monte-Carlo simulations using real-world highway and urban driving data. The results show that the proposed algorithm can enhance the cybersecurity of CAVs by detecting cyberattacks on CAV platoons and isolating infrastructure-level traffic manipulation.
Learning Agent Interactions from Density Evolution in 3D Regions With Obstacles
Authors: Amoolya Tirumalai, Christos N. Mavridis, John S. Baras
Date: 2023-05-18 00:00:00

In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what current robots can do. Owing to the difficulty of observing and recovering the trajectories of the agents, we focus on the dynamics of their probability densities, which are governed by partial differential equations (PDEs), namely compressible Euler equations subject to non-local forces. We formulate the inverse problem of learning interactions as a PDE-constrained optimization problem of minimizing the squared Hellinger distance between the histogram of the flock and the distribution associated to our PDEs. The numerical methods used to efficiently solve the PDE-constrained optimization problem are described. Realistic flocking data are simulated using the Boids model of flocking agents, which differs in nature from the reconstruction models used in our PDEs. Our analysis and simulated experiments show that the behavior of cohesive flocks can be recovered accurately with approximate PDE solutions.
Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models
Authors: Christis Katsouris
Date: 2023-05-18 00:00:00
Tasks: regression, Time Series, Variable Selection

This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network.
The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research
Authors: Jonas Tallberg, Eva Erman, Markus Furendal, Johannes Geith, Mark Klamberg, Magnus Lundgren
Date: 2023-05-19 00:00:00

Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance.
AI Regulation in the European Union: Examining Non-State Actor Preferences
Authors: Jonas Tallberg, Magnus Lundgren, Johannes Geith
Date: 2023-05-19 00:00:00

As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.
Weighing Anchor on Credit Card Debt
Authors: Benedict Guttman-Kenney, Jesse Leary, Neil Stewart
Date: 2023-05-19 00:00:00

We find it is common for consumers who are not in financial distress to make credit card payments at or close to the minimum. This pattern is difficult to reconcile with economic factors but can be explained by minimum payment information presented to consumers acting as an anchor that weighs payments down. Building on Stewart (2009), we conduct a hypothetical credit card payment experiment to test an intervention to de-anchor payment choices. This intervention effectively stops consumers selecting payments at the contractual minimum. It also increases their average payments, as well as shifting the distribution of payments. By de-anchoring choices from the minimum, consumers increasingly choose the full payment amount - which potentially seems to act as a target payment for consumers. We innovate by linking the experimental responses to survey responses on financial distress and to actual credit card payment behaviours. We find that the intervention largely increases payments made by less financially-distressed consumers. We are also able to evaluate the potential external validity of our experiment and find that hypothetical responses are closely related to consumers' actual credit card payments.
The Over-and-Above Implementation of Reserve Policy in India
Authors: Orhan Aygün, Bertan Turhan
Date: 2023-05-19 00:00:00

The over-and-above choice rule is the prominent selection procedure to implement affirmative action. In India, it is legally mandated to allocate public school seats and government job positions. This paper presents an axiomatic characterization of the over-and-above choice rule by rigorously stating policy goals as formal axioms. Moreover, we characterize the deferred acceptance mechanism coupled with the over-and-above choice rules for centralized marketplaces.
Interviewing Matching in Random Markets
Authors: Maxwell Allman, Itai Ashlagi
Date: 2023-05-18 00:00:00

In many centralized labor markets candidates interview with potential employers before matches are formed through a clearinghouse One prominent example is the market for medical residencies and fellowships, which in recent years has had a large increase in the number of interviews. There have been numerous efforts to reduce the cost of interviewing in these markets using a variety of signalling mechanisms, however, the theoretical properties of these mechanisms have not been systematically studied in models with rich preferences. In this paper we give theoretical guarantees for a variety of mechanisms, finding that these mechanisms must properly balance competition. We consider a random market in which agents' latent preferences are based on observed qualities, personal taste and (ex post) interview shocks and assume that following an interview mechanism a final stable match is generated with respect to preferences over interview partners. We study a novel many-to-many interview match mechanism to coordinate interviews and that with relatively few interviews, when suitably designed, the interview match yields desirable properties. We find that under the interview matching mechanism with a limit of $k$ interviews per candidate and per position, the fraction of positions that are unfilled vanishes quickly with $k$. Moreover the ex post efficiency grows rapidly with $k$, and reporting sincere pre-interview preferences to this mechanism is an $\epsilon$-Bayes Nash equilibrium. Finally, we compare the performance of the interview match to other signalling and coordination mechanisms from the literature.
Delay-penalized CTC implemented based on Finite State Transducer
Authors: Zengwei Yao, Wei Kang, Fangjun Kuang, Liyong Guo, Xiaoyu Yang, Yifan Yang, Long Lin, Daniel Povey
Date: 2023-05-19 00:00:00
Tasks: Attribute

Connectionist Temporal Classification (CTC) suffers from the latency problem when applied to streaming models. We argue that in CTC lattice, the alignments that can access more future context are preferred during training, thereby leading to higher symbol delay. In this work we propose the delay-penalized CTC which is augmented with latency penalty regularization. We devise a flexible and efficient implementation based on the differentiable Finite State Transducer (FST). Specifically, by attaching a binary attribute to CTC topology, we can locate the frames that firstly emit non-blank tokens on the resulting CTC lattice, and add the frame offsets to the log-probabilities. Experimental results demonstrate the effectiveness of our proposed delay-penalized CTC, which is able to balance the delay-accuracy trade-off. Furthermore, combining the delay-penalized transducer enables the CTC model to achieve better performance and lower latency. Our work is open-sourced and publicly available https://github.com/k2-fsa/k2.
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes
Authors: Shun Zhang, Haoran Sun, Runze Yu, Hongshenyuan Cui, Jian Ren, Feifei Gao, Shi Jin, Hongxiang Xie, Hao Wang
Date: 2023-05-19 00:00:00
Tasks: Intelligent Communication, Quantization

In this paper, we propose a two-bit reconfigurable intelligent surface (RIS)-aided communication system, which mainly consists of a two-bit RIS, a transmitter and a receiver. A corresponding prototype verification system is designed to perform experimental tests in practical environments. The carrier frequency is set as 3.5GHz, and the RIS array possesses 256 units, each of which adopts two-bit phase quantization. In particular, we adopt a self-developed broadband intelligent communication system 40MHz-Net (BICT-40N) terminal in order to fully acquire the channel information. The terminal mainly includes a baseband board and a radio frequency (RF) front-end board, where the latter can achieve 26 dB transmitting link gain and 33 dB receiving link gain. The orthogonal frequency division multiplexing (OFDM) signal is used for the terminal, where the bandwidth is 40MHz and the subcarrier spacing is 625KHz. Also, the terminal supports a series of modulation modes, including QPSK, QAM, etc.Through experimental tests, we validate a few functions and properties of the RIS as follows. First, we validate a novel RIS power consumption model, which considers both the static and the dynamic power consumption. Besides, we demonstrate the existence of the imaging interference and find that two-bit RIS can lower the imaging interference about 10 dBm. Moreover, we verify that the RIS can outperform the metal plate in terms of the beam focusing performance. In addition, we find that the RIS has the ability to improve the channel stationarity. Then, we realize the multi-beam reflection of the RIS utilizing the pattern addition (PA) algorithm. Lastly, we validate the existence of the mutual coupling between different RIS units.
Sensing Aided Uplink Transmission in OTFS ISAC with Joint Parameter Association, Channel Estimation and Signal Detection
Authors: Xi Yang, Hang Li, Qinghua Guo, J. Andrew Zhang, Xiaojing Huang, Zhiqun Cheng
Date: 2023-05-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this work, we study sensing-aided uplink transmission in an integrated sensing and communication (ISAC) vehicular network with the use of orthogonal time frequency space (OTFS) modulation. To exploit sensing parameters for improving uplink communications, the parameters must be first associated with the transmitters, which is a challenging task. We propose a scheme that jointly conducts parameter association, channel estimation and signal detection by formulating it as a constrained bilinear recovery problem. Then we develop a message passing algorithm to solve the problem, leveraging the bilinear unitary approximate message passing (Bi-UAMP) algorithm. Numerical results validate the proposed scheme, which show that relevant performance bounds can be closely approached.
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
Authors: Fuwang Dong, Fan Liu, Shihang Lu, Weijie Yuan, Yuanhao Cui, Yifeng Xiong, Feifei Gao
Date: 2023-05-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

The integrated sensing and communication (ISAC) technique has the potential to achieve coordination gain by exploiting the mutual assistance between sensing and communication (S&C) functions. While the sensing-assisted communications (SAC) technology has been extensively studied for high-mobility scenarios, the communication-assisted sensing (CAS) counterpart remains widely unexplored. This paper presents a waveform design framework for CAS in 6G perceptive networks, aiming to attain an optimal sensing quality of service (QoS) at the user after the target's parameters successively ``pass-through'' the S$\&$C channels. In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget. The first scheme reveals a power allocation trade-off, while the latter presents a water-filling trade-off. Numerical results demonstrate the effectiveness of the proposed algorithms, where the dual-functional scheme exhibits approximately 25% performance gain compared to its separated waveform design counterpart.