pwc-archive/papers-with-abstracts huggingface dataset:
An Aliasing-Free Hybrid Digital-Analog Polyphonic Synthesizer
Authors: Jonas Roth, Domenic Keller, Oscar Castañeda, Christoph Studer
Date: 2023-11-30 00:00:00

Analog subtractive synthesizers are generally considered to provide superior sound quality compared to digital emulations. However, analog circuitry requires calibration and suffers from aging, temperature instability, and limited flexibility in generating a wide variety of waveforms. Digital synthesis can mitigate many of these drawbacks, but generating arbitrary aliasing-free waveforms remains challenging. In this paper, we present the +-synth, a hybrid digital-analog eight-voice polyphonic synthesizer prototype that combines the best of both worlds. At the heart of the synthesizer is the big Fourier oscillator (BFO), a novel digital very-large scale integration (VLSI) design that utilizes additive synthesis to generate a wide variety of aliasing-free waveforms. Each BFO produces two voices, using four oscillators per voice. A single oscillator can generate up to 1024 freely configurable partials (harmonic or inharmonic), which are calculated using coordinate rotation digital computers (CORDICs). The BFOs were fabricated as 65nm CMOS custom application-specific integrated circuits (ASICs), which are integrated in the +-synth to simultaneously generate up to 32768 partials. Four 24-bit 96kHz stereo DACs then convert the eight voices into the analog domain, followed by digitally controlled analog low-pass filtering and amplification. Measurement results of the +-synth prototype demonstrate high fidelity and low latency.
Generalized Lyapunov conditions for k-contraction: analysis and feedback design
Authors: Andreu Cecilia, Samuele Zoboli, Daniele Astolfi, Ulysse Serres, Vincent Andrieu
Date: 2023-11-30 00:00:00

Recently, the concept of k-contraction has been introduced as a promising generalization of contraction for dynamical systems. However, the study of k-contraction properties has faced significant challenges due to the reliance on complex mathematical objects called matrix compounds. As a result, related control design methodologies have yet to appear in the literature. In this paper, we overcome existing limitations and propose new sufficient conditions for k-contraction which do not require matrix compounds computation. Notably, these conditions are also necessary in the linear time-invariant framework. Leveraging on these findings, we propose a feedback design methodology for both the linear and the nonlinear scenarios which can be used to enforce k-contractivity properties on the closed-loop dynamics.
Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties
Authors: Yang Cui, Yang Xu, Yang Li, Yijian Wang, Xinpeng Zou
Date: 2023-11-30 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, Scheduling

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.
Robust Data-Driven Kalman Filtering for Unknown Linear Systems using Maximum Likelihood Optimization
Authors: Peihu Duan, Tao Liu, Yu Xing, Karl Henrik Johansson
Date: 2023-11-29 00:00:00
Tasks: State Estimation

This paper investigates the state estimation problem for unknown linear systems subject to both process and measurement noise. Based on a prior input-output trajectory sampled at a higher frequency and a prior state trajectory sampled at a lower frequency, we propose a novel robust data-driven Kalman filter (RDKF) that integrates model identification with state estimation for the unknown system. Specifically, the state estimation problem is formulated as a non-convex maximum likelihood optimization problem. Then, we slightly modify the optimization problem to get a problem solvable with a recursive algorithm. Based on the optimal solution to this new problem, the RDKF is designed, which can estimate the state of a given but unknown state-space model. The performance gap between the RDKF and the optimal Kalman filter based on known system matrices is quantified through a sample complexity bound. In particular, when the number of the pre-collected states tends to infinity, this gap converges to zero. Finally, the effectiveness of the theoretical results is illustrated by numerical simulations.
Game Projection and Robustness for Game-Theoretic Autonomous Driving
Authors: Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky
Date: 2023-11-29 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making

Game-theoretic approaches are envisioned to bring human-like reasoning skills and decision-making processes for autonomous vehicles (AVs). However, challenges including game complexity and incomplete information still remain to be addressed before they can be sufficiently practical for real-world use. Game complexity refers to the difficulties of solving a multi-player game, which include solution existence, algorithm convergence, and scalability. To address these difficulties, a potential game based framework was developed in our recent work. However, conditions on cost function design need to be enforced to make the game a potential game. This paper relaxes the conditions and makes the potential game approach applicable to more general scenarios, even including the ones that cannot be molded as a potential game. Incomplete information refers to the ego vehicle's lack of knowledge of other traffic agents' cost functions. Cost function deviations between the ego vehicle estimated/learned other agents' cost functions and their actual ones are often inevitable. This motivates us to study the robustness of a game-theoretic solution. This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated in a game without changing the optimality of the game solution. With this definition, closed-form robustness margins are derived. Numerical studies using highway lane-changing scenarios are reported.
Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
Authors: Yiting Chen, Liliaokeawawa Cothren, Jorge Cortes, Emiliano Dall'Anese
Date: 2023-11-29 00:00:00

This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem. To solve this regulation task, we design a state feedback controller based on a continuous approximation of the projected gradient flow. We first show that the equilibria of the interconnection between the plant and the proposed controller correspond to critical points of the constrained optimization problem. We then derive sufficient conditions to ensure that, for the closed-loop system, isolated locally optimal solutions of the optimization problem are locally exponentially stable and show that input constraints are satisfied at all times by identifying an appropriate forward-invariant set.
Optimizing the Generation and Transmission Capacity of Offshore Wind Parks under Weather Uncertainty
Authors: David Kröger, Jan Peper, Nils Offermann, Christian Rehtanz
Date: 2023-11-29 00:00:00

Offshore wind power in the North Sea is considered a main pillar in Europe's future energy system. A key challenge lies in determining the optimal spatial capacity allocation of offshore wind parks in combination with the dimensioning and layout of the connecting high-voltage direct current grid infrastructure. To determine economically cost optimal configurations, we apply an integrated capacity and transmission expansion problem within a pan-European electricity market and transmission grid model with a high spatial and temporal granularity. By conducting scenario analysis for the year 2030 with a gradually increasing CO2 price, possible offshore expansion paths are derived and presented. Special emphasis is laid on the effects of weather uncertainty by incorporating data from 21 historical weather years in the analysis. Two key findings are (i) an expansion in addition to the existing offshore wind capacity of 0 GW (136 EUR/tCO2), 12 GW (159 EUR/tCO2) and 30 GW (186 EUR/tCO2) dependent on the underlying CO2 price. (ii) A strong sensitivity of the results towards the underlying weather data highlighting the importance of incorporating multiple weather years.
Model Reduction of a Flexible Nonsmooth Oscillator Recovers its Entire Bifurcation Structure
Authors: Suparno Bhattacharyya, Joseph. P. Cusumano
Date: 2023-11-29 00:00:00

We study the reduced order modeling of a piecewise-linear, globally nonlinear flexible oscillator in which a Bernoulli-Euler beam is subjected to a position-triggered kick force and a piecewise restoring force at its tip. The nonsmooth boundary conditions, which determine different regions of a hybrid phase space, can generally be expected to excite many degrees of freedom. With kick strength as parameter, the system's bifurcation diagram is found to exhibit a range of periodic and chaotic behaviors. Proper orthogonal decomposition (POD) is used to obtain a single set of global basis functions spanning all of the hybrid regions. The reduced order model (ROM) dimension is chosen using previously developed energy closure analysis, ensuring approximate energy balance on the reduced subspace. This yields accurate ROMs with 8 degrees of freedom. Remarkably, we find that ROMs formulated using using data from individual periodic steady states can nevertheless be used to reconstruct the entire bifurcation structure of the original system without updating. This demonstrates that, despite being constructed with steady state data, the ROMs model sufficiently small transients with enough accuracy to permit using simple continuation for the bifurcation diagram. We also find ROM subspaces obtained for different values of the bifurcation parameter are essentially identical. Thus, POD augmented with energy closure analysis is found to reliably yield effective dimension estimates and ROMs for this nonlinear, nonsmooth system that are robust across stability transitions, including even period doubling cascades to chaos, thereby greatly reducing data requirements and computational costs.
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit
Authors: Japan K. Patel, Athi Varuttamaseni, Robert W. Youngblood III, John C. Lee
Date: 2023-11-28 00:00:00
Tasks: Anomaly Detection

A Python interface is developed for the GPWR Simulator to automatically simulate cyber-spoofing of different steam generator parameters and plant operation. Specifically, steam generator water level, feedwater flowrate, steam flowrate, valve position, and steam generator controller parameters, including controller gain and time constant, can be directly attacked using command inject, denial of service, and man-in-the-middle type attacks. Plant operation can be initialized to any of the initial conditions provided by the GPWR simulator. Several different diagnostics algorithms have been implemented for anomaly detection, including physics-based diagnostics with Kalman filtering, data-driven diagnostics, noise profiling, and online sensor validation. Industry-standard safety analysis code RELAP5 is also available as a part of the toolkit. Diagnostics algorithms are analyzed based on accuracy and efficiency. Our observations indicate that physics-based diagnostics with Kalman filtering are the most robust. An experimental quantum kernel has been added to the framework for preliminary testing. Our first impressions suggest that while quantum kernels can be accurate, just like any other kernels, their applicability is problem/data dependent, and can be prone to overfitting.
Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets
Authors: Julius Luy, Gerhard Hiermann, Maximilian Schiffer
Date: 2023-11-28 00:00:00

Nowadays, logistics service providers (LSPs) increasingly consider using a crowdsourced workforce on the last mile to fulfill customers' expectations regarding same-day or on-demand delivery at reduced costs. The crowdsourced workforce's availability is, however, uncertain. Therefore, LSPs often hire additional fixed employees to perform deliveries when the availability of crowdsourced drivers is low. In this context, the reliability versus flexibility trade-off which LSPs face over a longer period, e.g., a year, remains unstudied. Against this background, we jointly study a workforce planning problem that considers fixed drivers (FDs) and the temporal development of the crowdsourced driver (CD) fleet over a long-term time horizon. We consider two types of CDs, gigworkers (GWs) and occasional drivers (ODs). While GWs are not sensitive to the request's destination and typically exhibit high availability, ODs only serve requests whose origin and destination coincide with their own private route's origin and destination. Moreover, to account for time horizon-specific dynamics, we consider stochastic turnover for both FDs and CDs as well as stochastic CD fleet growth. We formulate the resulting workforce planning problem as a Markov decision process (MDP) whose reward function reflects total costs, i.e., wages and operational costs arising from serving demand with FDs and CDs, and solve it via approximate dynamic programming (ADP). Applying our approach to an environment based on real-world demand data from GrubHub, we find that in fleets consisting of FDs and CDs, ADP-based hiring policies can outperform myopic hiring policies by up to 19% in total costs. In the studied setting, we observed that GWs reduce the LSP's total costs more than ODs. When we account for CDs' increased resignation probability when not being matched with enough requests, the amount of required FDs increases.
New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance
Authors: Quinn DuPont
Date: 2023-11-25 00:00:00

This research examines the polycentric governance of digital assets in blockchain-based Decentralized Autonomous Organizations (DAOs). It offers a theoretical framework and addresses a critical challenge facing decentralized governance by developing a method to identify Sybils, or spurious identities. Sybils pose significant organizational sustainability threats to DAOs and other, commons-based online communities, and threat models are identified. The experimental method uses an autoencoder architecture and graph deep learning techniques to identify Sybil activity in a DAO governance dataset (snapshot.org). Specifically, a Graph Convolutional Neural Network (GCNN) learned voting behaviours and a fast vector clustering algorithm used high-dimensional embeddings to identify similar nodes in a graph. The results reveal that deep learning can effectively identify Sybils, reducing the voting graph by 2-5%. This research underscores the importance of Sybil resistance in DAOs, identifies challenges and opportunities for forensics and analysis of anonymous networks, and offers a novel perspective on decentralized governance, informing future policy, regulation, and governance practices.
Closed-Loop Ramp-Comparison Current Regulator for an Induction Machine with a PWM Voltage-Source Inverter
Authors: Aidar Zhetessov
Date: 2023-11-23 00:00:00

This paper addresses the closed-loop ramp comparison current regulation in an induction machine fed by a pulse width modulated voltage source inverter. The regulator is implemented in a synchronous frame, serving as a foundation for an overarching vector control of the induction machine. First, the effect of PI regulator gains on the controller performance is analyzed both theoretically and numerically using the developed Simulink model of the system. Next, the paper deals with high speed and/or low-voltage operating conditions of the machine, introducing the concept of overmodulation and analyzing its impact on the regulator performance. Obtained simulation results coincide with model-based theoretical predictions and literature findings. Finally, the work proposes an outlook for the high-speed system enhancements in terms of power electronics topology, control and modulation.
Grid-Forming Control of Power Converters: Equivalence Proof through Simplified Models
Authors: Aidar Zhetessov
Date: 2023-11-23 00:00:00

This work establishes the equivalence of selected grid-forming control algorithms within the context of simplified theoretical models. Considered algorithms are droop control, Virtual Synchronous Machine (VSM) and matching control. It is shown that nodal and network dynamics under those regulators boil down to the same equations near the selected (trivial) nominal operating point. Finally, some practical insights on each regulator dynamics and an outlook are provided.
AI-predicted protein deformation encodes energy landscape
Authors: John M McBride, Tsvi Tlusty
Date: 2023-11-30 00:00:00
Tasks: Protein Design

AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy $\Delta\Delta G$. Unexpectedly, we find that physical strain alone -- without any additional data or computation -- correlates almost as well with $\Delta\Delta G$ as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution.
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Authors: Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly, Joshua M. Susskind, Miguel Angel Bautista
Date: 2023-11-27 00:00:00

We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a diffusion generative model directly on 3D atomic positions without making assumptions about the explicit structure of molecules (e.g. modeling torsional angles) we are able to radically simplify structure learning, and make it trivial to scale up the model sizes. This model, called Molecular Conformer Fields (MCF), works by parameterizing conformer structures as functions that map elements from a molecular graph directly to their 3D location in space. This formulation allows us to boil down the essence of structure prediction to learning a distribution over functions. Experimental results show that scaling up the model capacity leads to large gains in generalization performance without enforcing inductive biases like rotational equivariance. MCF represents an advance in extending diffusion models to handle complex scientific problems in a conceptually simple, scalable and effective manner.
Bootstrap Inference on Partially Linear Binary Choice Model
Authors: Wenzheng Gao, Zhenting Sun
Date: 2023-11-30 00:00:00

The partially linear binary choice model can be used for estimating structural equations where nonlinearity may appear due to diminishing marginal returns, different life cycle regimes, or hectic physical phenomena. The inference procedure for this model based on the analytic asymptotic approximation could be unreliable in finite samples if the sample size is not sufficiently large. This paper proposes a bootstrap inference approach for the model. Monte Carlo simulations show that the proposed inference method performs well in finite samples compared to the procedure based on the asymptotic approximation.
Identification in Endogenous Sequential Treatment Regimes
Authors: Pedro Picchetti
Date: 2023-11-30 00:00:00

This paper develops a novel nonparametric identification method for treatment effects in settings where individuals self-select into treatment sequences. I propose an identification strategy which relies on a dynamic version of standard Instrumental Variables (IV) assumptions and builds on a dynamic version of the Marginal Treatment Effects (MTE) as the fundamental building block for treatment effects. The main contribution of the paper is to relax assumptions on the support of the observed variables and on unobservable gains of treatment that are present in the dynamic treatment effects literature. Monte Carlo simulation studies illustrate the desirable finite-sample performance of a sieve estimator for MTEs and Average Treatment Effects (ATEs) on a close-to-application simulation study.
Extrapolating Away from the Cutoff in Regression Discontinuity Designs
Authors: Yiwei Sun
Date: 2023-11-29 00:00:00
Tasks: counterfactual, regression, Sensitivity

Canonical RD designs yield credible local estimates of the treatment effect at the cutoff under mild continuity assumptions, but they fail to identify treatment effects away from the cutoff without additional assumptions. The fundamental challenge of identifying treatment effects away from the cutoff is that the counterfactual outcome under the alternative treatment status is never observed. This paper aims to provide a methodological blueprint to identify treatment effects away from the cutoff in various empirical settings by offering a non-exhaustive list of assumptions on the counterfactual outcome. Instead of assuming the exact evolution of the counterfactual outcome, this paper bounds its variation using the data and sensitivity parameters. The proposed assumptions are weaker than those introduced previously in the literature, resulting in partially identified treatment effects that are less susceptible to assumption violations. This approach accommodates both single cutoff and multi-cutoff designs. The specific choice of the extrapolation assumption depends on the institutional background of each empirical application. Additionally, researchers are recommended to conduct sensitivity analysis on the chosen parameter and assess resulting shifts in conclusions. The paper compares the proposed identification results with results using previous methods via an empirical application and simulated data. It demonstrates that set identification yields a more credible conclusion about the sign of the treatment effect.