pwc-archive/papers-with-abstracts huggingface dataset:
School Choice with Farsighted Students
Authors: Ata Atay, Ana Mauleon, Vincent Vannetelbosch
Date: 2022-12-14 00:00:00

We consider priority-based school choice problems with farsighted students. We show that a singleton set consisting of the matching obtained from the Top Trading Cycles (TTC) mechanism is a farsighted stable set. However, the matching obtained from the Deferred Acceptance (DA) mechanism may not belong to any farsighted stable set. Hence, the TTC mechanism provides an assignment that is not only Pareto efficient but also farsightedly stable. Moreover, looking forward three steps ahead is already sufficient for stabilizing the matching obtained from the TTC.
Beyond the Metaverse: XV (eXtended meta/uni/Verse)
Authors: Steve Mann, Yu Yuan, Tom Furness, Joseph Paradiso, Thomas Coughlin
Date: 2022-12-15 00:00:00

We propose the term and concept XV (eXtended meta/omni/uni/Verse) as an alternative to, and generalization of, the shared/social virtual reality widely known as ``metaverse''. XV is shared/social XR. We, and many others, use XR (eXtended Reality) as a broad umbrella term and concept to encompass all the other realities, where X is an ``anything'' variable, like in mathematics, to denote any reality, X $\in$ \{physical, virtual, augmented, \ldots \} reality. Therefore XV inherits this generality from XR. We begin with a very simple organized taxonomy of all these realities in terms of two simple building blocks: (1) physical reality (PR) as made of ``atoms'', and (2) virtual reality (VR) as made of ``bits''. Next we introduce XV as combining all these realities with extended society as a three-dimensional space and taxonomy of (1) ``atoms'' (physical reality), (2) ``bits'' (virtuality), and (3) ``genes'' (sociality). Thus those working in the liminal space between Virtual Reality (VR), Augmented Reality (AR), metaverse, and their various extensions, can describe their work and research as existing in the new field of XV. XV includes the metaverse along with extensions of reality itself like shared seeing in the infrared, ultraviolet, and shared seeing of electromagnetic radio waves, sound waves, and electric currents in motors. For example, workers in a mechanical room can look at a pump and see a superimposed time-varying waveform of the actual rotating magnetic field inside its motor, in real time, while sharing this vision across multiple sites. Presented at IEEE Standards Association, Behind and Beyond the Metaverse: XV (eXtended meta/uni/Verse), Thurs. Dec. 8, 2022, 2:15-3:30pm, EST.
Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks
Authors: Yunqi Wang, Yang Li, Qingjiang Shi, Yik-Chung Wu
Date: 2022-11-23 00:00:00
Tasks: Graph Neural Network

Cooperative beamforming design has been recognized as an effective approach in modern wireless networks to meet the dramatically increasing demand of various wireless data traffics. It is formulated as an optimization problem in conventional approaches and solved iteratively in an instance-by-instance manner. Recently, learning-based methods have emerged with real-time implementation by approximating the mapping function from the problem instances to the corresponding solutions. Among various neural network architectures, graph neural networks (GNNs) can effectively utilize the graph topology in wireless networks to achieve better generalization ability on unseen problem sizes. However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks. To fill this gap, we propose an edge-graph-neural-network (Edge-GNN) by incorporating an edge-update mechanism into the GNN, which learns the cooperative beamforming on the graph edges. Simulation results show that the proposed Edge-GNN achieves higher sum rate with much shorter computation time than state-of-the-art approaches, and generalizes well to different numbers of base stations and user equipments.
Influence of rationality levels on dynamics of heterogeneous Cournot duopolists with quadratic costs
Authors: Xiaoliang Li, Yihuo Jiang
Date: 2022-12-14 00:00:00

This paper is intended to investigate the dynamics of heterogeneous Cournot duopoly games, where the first players adopt identical gradient adjustment mechanisms but the second players are endowed with distinct rationality levels. Based on tools of symbolic computations, we introduce a new approach and use it to establish rigorous conditions of the local stability for these models. We analytically investigate the bifurcations and prove that the period-doubling bifurcation is the only possible bifurcation that may occur for all the considered models. The most important finding of our study is regarding the influence of players' rational levels on the stability of heterogeneous duopolistic competition. It is derived that the stability region of the model where the second firm is rational is the smallest, while that of the one where the second firm is boundedly rational is the largest. This fact is counterintuitive and contrasts with relative conclusions in the existing literature. Furthermore, we also provide numerical simulations to demonstrate the emergence of complex dynamics such as periodic solutions with different orders and strange attractors.
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
Authors: Darius Petermann, Gordon Wichern, Aswin Shanmugam Subramanian, Zhong-Qiu Wang, Jonathan Le Roux
Date: 2022-12-14 00:00:00
Tasks: Action Detection, Activity Detection, Audio Source Separation, Audio Tagging, speech-recognition, Speech Recognition

Emulating the human ability to solve the cocktail party problem, i.e., focus on a source of interest in a complex acoustic scene, is a long standing goal of audio source separation research. Much of this research investigates separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. In this paper, we focus on the cocktail fork problem, which takes a three-pronged approach to source separation by separating an audio mixture such as a movie soundtrack or podcast into the three broad categories of speech, music, and sound effects (SFX - understood to include ambient noise and natural sound events). We benchmark the performance of several deep learning-based source separation models on this task and evaluate them with respect to simple objective measures such as signal-to-distortion ratio (SDR) as well as objective metrics that better correlate with human perception. Furthermore, we thoroughly evaluate how source separation can influence downstream transcription tasks. First, we investigate the task of activity detection on the three sources as a way to both further improve source separation and perform transcription. We formulate the transcription tasks as speech recognition for speech and audio tagging for music and SFX. We observe that, while the use of source separation estimates improves transcription performance in comparison to the original soundtrack, performance is still sub-optimal due to artifacts introduced by the separation process. Therefore, we thoroughly investigate how remixing of the three separated source stems at various relative levels can reduce artifacts and consequently improve the transcription performance. We find that remixing music and SFX interferences at a target SNR of 17.5 dB reduces speech recognition word error rate, and similar impact from remixing is observed for tagging music and SFX content.
Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model
Authors: Ying Xu, Samalika Perera, Yeshwanth Bethi, Saeed Afshar, André van Schaik
Date: 2022-12-14 00:00:00

This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlea models and leaky integrate and fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.
Bifurcations in the Herd Immunity Threshold for Discrete-Time Models of Epidemic Spread
Authors: Sinan A. Ozbay, Bjarke F. Nielsen, Maximilian M. Nguyen
Date: 2022-12-14 00:00:00

We performed a thorough sensitivity analysis of the herd immunity threshold for discrete-time SIR compartmental models with a static network structure. We find unexpectedly that these models violate classical intuition which holds that the herd immunity threshold should monotonically increase with the transmission parameter. We find the existence of bifurcations in the herd immunity threshold in the high transmission probability regime. The extent of these bifurcations is modulated by the graph heterogeneity, the recovery parameter, and the network size. In the limit of large, well-mixed networks, the behavior approaches that of difference equation models, suggesting this behavior is a universal feature of all discrete-time SIR models. These results suggest careful attention is needed in both selecting the assumptions on how to model time and heterogeneity in epidemiological models and the subsequent conclusions that can be drawn.
An Effective Methodology for Short-Circuit Calculation of Power Systems Dominated by Power Electronics Converters Considering Unbalanced Voltage Conditions and Converter Limits
Authors: Jie Song, Marc Cheah-Mane, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2022-12-15 00:00:00

This paper deals with the challenge of short-circuit calculation for power systems dominated by power electronics converters. A novel methodology has been presented to identify short-circuit equilibrium point of the studied system considering the operation and limitations of power converters. The studied system has been modeled with an element-based steady-state formulation. In particular, the governing equations implemented on converter control, which involve a specific control mode and various potential current-saturation states, are included. Then, an effective and efficient approach has been proposed to identify the short-circuit equilibrium points that satisfies converters limitations. Numerical case studies with VSCs show that the proposed methodology can identify the short-circuit equilibrium point efficiently and accurately with different types and depths of short-circuit fault. The short-circuit calculation results have been validated with dynamic simulations.
Online Feedback Optimization for Subtransmission Grid Control
Authors: Lukas Ortmann, Jean Maeght, Patrick Panciatici, Florian Dörfler, Saverio Bolognani
Date: 2022-12-15 00:00:00

The increasing electric power consumption and the shift towards renewable energy resources demand for new ways to operate transmission and subtransmission grids. Online Feedback Optimization (OFO) is a feedback control method that enables real-time, constrained, and optimal control of these grids. Such controllers can minimize, e.g., curtailment and losses while satisfying grid constraints like voltage and current limits. We tailor and extend the OFO control method to handle discrete inputs and explain how to design an OFO controller for the subtransmission grid. We present a novel and publicly available benchmark which is of the real French subtransmission grid on which we analyze the proposed controller in terms of robustness against model mismatch, constraint satisfaction, and tracking performance. Overall, we show that OFO controllers can help utilize the grid to its full extent, virtually reinforce it, and operate it optimally and in real-time by using flexibility offered by renewable generators connected to distribution grids.
Real-time Curative Actions for Power Systems via Online Feedback Optimization
Authors: Lukas Ortmann, Gianni Hotz, Saverio Bolognani, Florian Dörfler
Date: 2022-12-15 00:00:00

Curative or remedial actions are the set of immediate actions intended to bring the power grid to a safe operating point after a contingency. The effectiveness of these actions is essential to guarantee curative N-1 security. Nowadays, curative actions are derived ahead of time, based on the anticipated future grid state. Due to the shift from steady to volatile energy resources, the grid state will frequently change and the curative actions would need to be pre-planned increasingly often. Furthermore, with the shift from large bulk production to many small decentralized energy sources more devices need to be actuated simultaneously to achieve the same outcome. Instead of pre-planning, we propose to calculate these complex curative actions in real-time after the occurrence of a contingency. We show how the method of Online Feedback Optimization (OFO) is well suited for this task. As a preliminary demonstration of these capabilities, we use an (OFO) controller, that after a fault, reduces the voltage difference over a breaker to enable the operators to reclose it. This test case is inspired by the 2003 Swiss-Italian blackout, which was caused by a relatively minor incident followed by ineffective curative actions. Finally, we identify and discuss some open questions, including closed-loop stability and robustness to model mismatch.
Small Spacecraft for Global Greenhouse Gas Monitoring
Authors: Victoria Mayorova, Andrey Morozov, Iliya Golyak, Nikita Lazarev, Valeriia Melnikova, Dmitry Rachkin, Victor Svirin, Stepan Tenenbaum, Igor Fufurin
Date: 2022-12-15 00:00:00

This work is devoted to the capabilities analysis of constellation and small spacecraft developed using CubeSat technology to solve promising problems of the Earth remote sensing in the area of greenhouse gases emissions. This paper presents the scientific needs for such tasks, followed by descriptions and discussions of the micro-technology application both in the small satellite platform design and in the payload design. The overview of analogical spacecraft is carried out. The design of a new spacecraft for determination the oxygen and carbon dioxide concentration in the air column along the line of sight of the spacecraft when it illuminated by reflected sunlight is introduced. A mock-up of the device was made for greenhouse gases remote sensing a Fourier Transform Infrared (FTIR) spectroradiometer is placed in the small spacecraft design. The results of long-term measurements of greenhouse gas concentrations using the developed Fourier spectrometer mock-up is presented.
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions
Authors: Guanru Pan, Ruchuan Ou, Timm Faulwasser
Date: 2022-12-15 00:00:00
Tasks: LEMMA

The paper investigates data-driven output-feedback predictive control of linear systems subject to stochastic disturbances. The scheme relies on the recursive solution of a suitable data-driven reformulation of a stochastic Optimal Control Problem (OCP), which allows for forward prediction and optimization of statistical distributions of inputs and outputs. Our approach avoids the use of parametric system models. Instead it is based on previously recorded data using a recently proposed stochastic variant of Willems' fundamental lemma. The stochastic variant of the lemma is applicable to a large class of linear dynamics subject to stochastic disturbances of Gaussian and non-Gaussian nature. To ensure recursive feasibility, the initial condition of the OCP -- which consists of information about past inputs and outputs -- is considered as an extra decision variable of the OCP. We provide sufficient conditions for recursive feasibility and closed-loop practical stability of the proposed scheme as well as performance bounds. Finally, a numerical example illustrates the efficacy and closed-loop properties of the proposed scheme.
Hybrid stability augmentation control of multi-rotor UAV in confined space based on adaptive backstepping control
Authors: QuanXi Zhan, JunRui Zhang, ChenYang Sun, RunJie Shen, Bin He
Date: 2022-12-15 00:00:00

This paper applies the UAV to the inspection of water diversion pipelines in hydropower stations. The diversion pipeline is an enclosed space, so the airflow disturbance caused by the rotation of the UAV blades and the strong air convection from the chimney effect have a great impact on the flight control of the UAV. Although the traditional linear control PID flight control algorithm has been widely used and can meet the requirements of general flight tasks, it cannot guarantee the stability of the system over a wide range. The inspection of a diversion line in an enclosed space requires high system stability and robustness of the UAV controller. In this paper, a hybrid stabilised adaptive backstepping control method is proposed. Firstly, a multi-rotor UAV model is analysed and transformed into a strict feedback form with external disturbances; then adaptive techniques are used to estimate the airflow disturbances caused by the blades, and the attitude and position tracking controllers are designed by combining backstepping control and PID control respectively; finally, the asymptotic stability of the system is ensured by constructing a Lyapunov function. The experimental data show that the flight controller designed in this paper has good robustness and tracking performance, and can better resist the disturbance caused by airflow disturbance in confined space.
Multi-objective low-thrust spacecraft trajectory design using reachability analysis
Authors: Nikolaus Vertovec, Sina Ober-Blöbaum, Kostas Margellos
Date: 2022-12-14 00:00:00

One of the fundamental problems in spacecraft trajectory design is finding the optimal transfer trajectory that minimizes the propellant consumption and transfer time simultaneously. We formulate this as a multi-objective optimal control (MOC) problem that involves optimizing over the initial or final state, subject to state constraints. Drawing on recent developments in reachability analysis subject to state constraints, we show that the proposed MOC problem can be stated as an optimization problem subject to a constraint that involves the sub-level set of the viscosity solution of a quasi-variational inequality. We then generalize this approach to account for more general optimal control problems in Bolza form. We relate these problems to the Pareto front of the developed multi-objective programs. The proposed approach is demonstrated on two low-thrust orbital transfer problems around a rotating asteroid.
Robust Multitarget Tracking in Interference Environments: A Message-Passing Approach
Authors: Xianglong Bai, Hua Lan, Zengfu Wang, Quan Pan, Yuhang Hao, Can Li
Date: 2022-12-14 00:00:00

Multitarget tracking in the interference environments suffers from the nonuniform, unknown and time-varying clutter, resulting in dramatic performance deterioration. We address this challenge by proposing a robust multitarget tracking algorithm, which estimates the states of clutter and targets simultaneously by the message-passing (MP) approach. We define the non-homogeneous clutter with a finite mixture model containing a uniform component and multiple nonuniform components. The measured signal strength is utilized to estimate the mean signal-to-noise ratio (SNR) of targets and the mean clutter-to-noise ratio (CNR) of clutter, which are then used as additional feature information of targets and clutter to improve the performance of discrimination of targets from clutter. We also present a hybrid data association which can reason over correspondence between targets, clutter, and measurements. Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part. As a result, a closed-loop iterative optimization of the posterior probability distribution can be obtained, which can effectively deal with the coupling between target tracking, clutter estimation and data association. Simulation results demonstrate the performance superiority and robustness of the proposed multitarget tracking algorithm compared with the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter.
A Predictive Operation Controller for an Electro-Thermal Microgrid Utilizing Variable Flow Temperatures
Authors: Max Rose, Christian A. Hans, Johannes Schiffer
Date: 2022-12-14 00:00:00
Tasks: Model Predictive Control

We propose an optimal operation control strategy for an electro-thermal microgrid. Compared to existing work, our approach increases flexibility by operating the thermal network with variable flow temperatures and in that way explicitly exploits its inherent storage capacities. To this end, the microgrid is represented by a multi-layer network composed of an electrical and a thermal layer. We show that the system behavior can be represented by a discrete-time state model derived from DC power flow approximations and 1d incompressible Euler equations. Both layers are interconnected via heat pumps. By combining this model with desired operating objectives and constraints, we obtain a constrained convex optimization problem. This is used to derive a model predictive control scheme for the optimal operation of electro-thermal microgrids. The performance of the proposed operation control algorithm is demonstrated in a numerical case study.
Further characterizations of integral input-to-state stability for hybrid systems
Authors: Shenyu Liu, Antonio Russo
Date: 2022-12-14 00:00:00

In this work we present further characterizations of integral input-to-state stability (iISS) for hybrid systems. In particular, the equivalence between 0-input uniform global asymptotic stability (0-UGAS) plus uniform bounded energy bounded state (UBEBS) and iISS is examined. In order to show this equivalence, some necessary conditions for 0-UGAS and UBEBS are provided. In addition, a non-smooth Lyapunov characterization for hybrid systems is proposed and proven. With the help of the aforementioned equivalence, the combination of local iISS and practical iISS, which are defined in this work, is also shown to be equivalent to iISS under one condition on the local and practical quantifiers.
Market Mechanisms for Low-Carbon Electricity Investments: A Game-Theoretical Analysis
Authors: Dongwei Zhao, Sarah Coyle, Apurba Sakti, Audun Botterud
Date: 2022-12-14 00:00:00

Electricity markets are transforming from the dominance of conventional energy resources (CERs), e.g., fossil fuels, to low-carbon energy resources (LERs), e.g., renewables and energy storage. This work examines market mechanisms to incentivize LER investments, while ensuring adequate market revenues for investors, guiding investors' strategic investments towards social optimum, and protecting consumers from scarcity prices. To reduce the impact of excessive scarcity prices, we present a new market mechanism, which consists of a Penalty payment for lost load, a supply Incentive, and an energy price Uplift (PIU). We establish a game-theoretical framework to analyze market equilibrium. We prove that one Nash equilibrium under the penalty payment and supply incentive can reach the social optimum given quadratic supply costs of CERs. Although the price uplift can ensure adequate revenues, the resulting system cost deviates from the social optimum while the gap decreases as more CERs retire. Furthermore, under the traditional marginal-cost pricing (MCP) mechanism, investors may withhold investments to cause scarcity prices, but such behavior is absent under the PIU mechanism. Simulation results show that the PIU mechanism can reduce consumers' costs by over 30% compared with the MCP mechanism by reducing excessive revenues of low-cost CERs from scarcity prices.