pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Reverberation as Supervision for Unsupervised Speech Separation
Authors: Kohei Saijo, Gordon Wichern, François G. Germain, Zexu Pan, Jonathan Le Roux
Date: 2024-08-06 00:00:00
Tasks: Speech Separation

Reverberation as supervision (RAS) is a framework that allows for training monaural speech separation models from multi-channel mixtures in an unsupervised manner. In RAS, models are trained so that sources predicted from a mixture at an input channel can be mapped to reconstruct a mixture at a target channel. However, stable unsupervised training has so far only been achieved in over-determined source-channel conditions, leaving the key determined case unsolved. This work proposes enhanced RAS (ERAS) for solving this problem. Through qualitative analysis, we found that stable training can be achieved by leveraging the loss term to alleviate the frequency-permutation problem. Separation performance is also boosted by adding a novel loss term where separated signals mapped back to their own input mixture are used as pseudo-targets for the signals separated from other channels and mapped to the same channel. Experimental results demonstrate high stability and performance of ERAS.
Certification of Linear Inclusions for Nonlinear Systems
Authors: Yehia Abdelsalam, Sebastian Engell
Date: 2024-08-07 00:00:00

In this work, we propose novel method for certifying if a given set of vertex linear systems constitute a linear difference inclusion for a nonlinear system. The method relies on formulating the verification of the inclusion as an optimization problem in a novel manner. The result is a Yes/No certificate. We illustrate how the method can be useful in obtaining less conservative linear enclosures for nonlinear systems.
Distributed Feedback-Feedforward Algorithms for Time-Varying Resource Allocation
Authors: Yiqiao Xu, Tengyang Gong, Zhengtao Ding, Alessandra Parisio
Date: 2024-08-07 00:00:00

In this paper, we address distributed Time-Varying Resource Allocation (TVRA) problem, where the local cost functions, global equality constraint, and Local Feasibility Constraints (LFCs) vary with time. To track the optimal trajectories, algorithms that mimic the structure of feedback-feedforward control systems are proposed. We begin with their conceptual design in the absence of LFCs, developing a feedback-feedforward algorithm that is fixed-time convergent. For cases with LFCs, existing approaches predominantly rely on constructing a time-dependent barrier function, which may impede the design of fixed-time convergent algorithms. Therefore, by exploring the connection between projection and penalty functions, switched feedforward laws are tailored to handle LFCs, with projection used in conjunction. Based on this, we develop a projection-based feedback-feedforward algorithm, which converges to the exact optimal trajectories, possibly along with a number of switching instants, while exhibiting fixed-time convergence between consecutive switching instants. Numerical experiments verify the effectiveness of the proposed algorithms.
GAIA -- A Large Language Model for Advanced Power Dispatch
Authors: Yuheng Cheng, Huan Zhao, Xiyuan Zhou, Junhua Zhao, Yuji Cao, Chao Yang
Date: 2024-08-07 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, Management

Power dispatch is essential for providing stable, cost-effective, and eco-friendly electricity to society. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper introduces GAIA, the pioneering Large Language Model (LLM) tailored for power dispatch tasks. We have developed a novel dataset construction technique that harnesses a range of data sources to fine-tune GAIA for optimal performance in this domain. This approach streamlines LLM training, allowing for the seamless integration of multidimensional data in power system management. Additionally, we have crafted specialized prompt strategies to boost GAIA's input-output efficiency in dispatch scenarios. When evaluated on the ElecBench benchmark, GAIA surpasses the baseline model LLaMA2 on multiple metrics. In practical applications, GAIA has demonstrated its ability to enhance decision-making processes, improve operational efficiency, and facilitate better human-machine interactions in power dispatch operations. This paper expands the application of LLMs to power dispatch and validates their practical utility, paving the way for future innovations in this field.
Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
Authors: Gabriele Dessena, Marco Civera
Date: 2024-08-07 00:00:00

In the field of Structural Dynamics, modal analysis is the foundation of System Identification and vibration-based inspection. However, despite their widespread use, current state-of-the-art methods for extracting modal parameters from multi-input multi-output (MIMO) frequency domain data are still affected by many technical limitations. Mainly, they can be computationally cumbersome and/or negatively affected by close-in-frequency modes. The Loewner Framework (LF) was recently proposed to alleviate these problems with the limitation of working with single-input data only. This work proposes a computationally improved version of the LF, or iLF, to extract modal parameters more efficiently. Also, the proposed implementation is extended in order to handle MIMO data in the frequency domain. This new implementation is compared to state-of-the-art methods such as the frequency domain implementations of the Least Square Complex Exponential method and the Numerical Algorithm for Subspace State Space System Identification on numerical and experimental datasets. More specifically, a finite element model of a 3D Euler-Bernoulli beam is used for the baseline comparison and the noise robustness verification of the proposed MIMO iLF algorithm. Then, an experimental dataset from MIMO ground vibration tests of a trainer jet aircraft with over 91 accelerometer channels is chosen for the algorithm validation on a real-life application. Its validation is carried out with known results from a single-input multi-output dataset of the starboard wing of the same aircraft. Excellent results are achieved in terms of accuracy, robustness to noise, and computational performance by the proposed improved MIMO method, both on the numerical and the experimental datasets. The MIMO iLF MATLAB implementation is shared in the work supplementary material.
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling
Authors: Mingxue Yan, Minghao Han, Adrian Wing-Keung Law, Xunyuan Yin
Date: 2024-08-07 00:00:00
Tasks: Chemical Process, Physics-informed machine learning

In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks - the state lifting network and the noise characterization network - using both data and available physical information. The two neural networks account for the nonlinear lifting functions for Koopman modeling and describing system noise distributions, respectively. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behavior of the nonlinear system. Based on the Koopman model, a self-tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pre-trained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient because only convex optimization is involved during online implementation, and updating the weighting matrices of the MHE scheme does not require re-training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.
Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware Acceleration
Authors: Zhongyao Luo, Hao Wu, Zhao Ge, Ming Tang
Date: 2024-08-07 00:00:00
Tasks: GPU, Intrusion Detection, Knowledge Distillation

Fiber-optic sensing, especially distributed optical fiber vibration (DVS) sensing, is gaining importance in internet of things (IoT) applications, such as industrial safety monitoring and intrusion detection. Despite their wide application, existing post-processing methods that rely on deep learning models for event recognition in DVS systems face challenges with real-time processing of large sample data volumes, particularly in long-distance applications. To address this issue, we propose to use a four-layer convolutional neural network (CNN) with ResNet as the teacher model for knowledge distillation. This results in a significant improvement in accuracy, from 83.41% to 95.39%, on data from previously untrained environments. Additionally, we propose a novel hardware design based on field-programmable gate arrays (FPGA) to further accelerate model inference. This design replaces multiplication with binary shift operations and quantizes model weights, enabling high parallelism and low latency. Our implementation achieves an inference time of 0.083 ms for a spatial-temporal sample covering a 12.5 m fiber length and 0.256 s time frame. This performance enables real-time signal processing over approximately 38.55 km of fiber, about $2.14\times$ the capability of an Nvidia GTX 4090 GPU. The proposed method greatly enhances the efficiency of vibration pattern recognition, promoting the use of DVS as a smart IoT system. The data and code are available at https://github.com/HUST-IOF/Efficient-DVS.
Goal-oriented Semantic Communication for the Metaverse Application
Authors: Zhe Wang, Nan Li, Yansha Deng
Date: 2024-08-07 00:00:00
Tasks: NeRF, Semantic Communication

With the emergence of the metaverse and its role in enabling real-time simulation and analysis of real-world counterparts, an increasing number of personalized metaverse scenarios are being created to influence entertainment experiences and social behaviors. However, compared to traditional image and video entertainment applications, the exact transmission of the vast amount of metaverse-associated information significantly challenges the capacity of existing bit-oriented communication networks. Moreover, the current metaverse also witnesses a growing goal shift for transmitting the meaning behind custom-designed content, such as user-designed buildings and avatars, rather than exact copies of physical objects. To meet this growing goal shift and bandwidth challenge, this paper proposes a goal-oriented semantic communication framework for metaverse application (GSCM) to explore and define semantic information through the goal levels. Specifically, we first analyze the traditional image communication framework in metaverse construction and then detail our proposed semantic information along with the end-to-end wireless communication. We then describe the designed modules of the GSCM framework, including goal-oriented semantic information extraction, base knowledge definition, and neural radiance field (NeRF) based metaverse construction. Finally, numerous experiments have been conducted to demonstrate that, compared to image communication, our proposed GSCM framework decreases transmission latency by up to 92.6% and enhances the virtual object operation accuracy and metaverse construction clearance by up to 45.6% and 44.7%, respectively.
Active Compensation of Position Dependent Flexible Dynamics in High-Precision Mechatronics
Authors: Yorick Broens, Hans Butler, Ramidin Kamidi, Koen Verkerk, Siep Weiland
Date: 2024-08-07 00:00:00
Tasks: Position

Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage.
Control of SIR Epidemics: Sacrificing Optimality for Feasibility
Authors: Baike She, Lei Xin, Shreyas Sundaram, Philip E. Paré
Date: 2024-08-06 00:00:00
Tasks: parameter estimation

We study the impact of parameter estimation and state measurement errors on a control framework for optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider isolation as the control strategy. We use a control strategy to remove (isolate) a portion of the infected population. Our goal is to maintain the daily infected population below a certain level, while minimizing the resource captured by the isolation rate. Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a parameter estimation strategy and further characterize the parameter estimation error bound. In order to deal with uncertainties, we propose a robust control strategy by overestimating the seriousness of the epidemic and study the feasibility of the system. Compared to the optimal control strategy, we establish that the proposed strategy under parameter estimation and measurement errors will sacrifice optimality to flatten the curve.
Electricity Market-Clearing With Extreme Events
Authors: Tomas Tapia, Zhirui Liang, Charalambos Konstantinou, Yury Dvorkin
Date: 2024-08-06 00:00:00
Tasks: Scheduling

Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare events. Efficiently maintaining the reliability of renewable-dominant power systems during extreme weather events requires co-optimizing system resources, while differentiating between large/rare and small/frequent deviations from forecast conditions. To address this gap in both research and practice, we propose managing the uncertainties associated with extreme weather events through an additional reserve service, termed extreme reserve. The procurement of extreme reserve is co-optimized with energy and regular reserve using a large deviation theory chance-constrained (LDT-CC) model, where LDT offers a mathematical framework to quantify the increased uncertainty during extreme events. To mitigate the high additional costs associated with reserve scheduling under the LDT-CC model, we also propose an LDT model based on weighted chance constraints (LDT-WCC). This model prepares the power system for extreme events at a lower cost, making it a less conservative alternative to the LDT-CC model. The proposed market design leads to a competitive equilibrium while ensuring cost recovery. Numerical experiments on an illustrative system and a modified 8-zone ISO New England system highlight the advantages of the proposed market design.
Signal Attenuation through Foliage Estimator (SAFE)
Authors: Mathieu Châteauvert, Jonathan Ethier, Pierre Bouchard
Date: 2024-08-07 00:00:00

The SAFE tool is an open-source Radio Frequency (RF) propagation model designed for path loss predictions in foliage-dominant environments. It utilizes the ITU-R P.1812-6 model as its backbone, enhances predictions with the physics-based Radiative Energy Transfer (RET) model and makes use of high-resolution terrain and clutter elevation datasets
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens
Authors: Zhan Gao, Hyungsik Roger Moon
Date: 2024-08-07 00:00:00
Tasks: Combinatorial Optimization, Computational Efficiency

This paper addresses the robust estimation of linear regression models in the presence of potentially endogenous outliers. Through Monte Carlo simulations, we demonstrate that existing $L_1$-regularized estimation methods, including the Huber estimator and the least absolute deviation (LAD) estimator, exhibit significant bias when outliers are endogenous. Motivated by this finding, we investigate $L_0$-regularized estimation methods. We propose systematic heuristic algorithms, notably an iterative hard-thresholding algorithm and a local combinatorial search refinement, to solve the combinatorial optimization problem of the \(L_0\)-regularized estimation efficiently. Our Monte Carlo simulations yield two key results: (i) The local combinatorial search algorithm substantially improves solution quality compared to the initial projection-based hard-thresholding algorithm while offering greater computational efficiency than directly solving the mixed integer optimization problem. (ii) The $L_0$-regularized estimator demonstrates superior performance in terms of bias reduction, estimation accuracy, and out-of-sample prediction errors compared to $L_1$-regularized alternatives. We illustrate the practical value of our method through an empirical application to stock return forecasting.
Robust Identification in Randomized Experiments with Noncompliance
Authors: Désiré Kédagni, Huan Wu, Yi Cui
Date: 2024-08-07 00:00:00

Instrument variable (IV) methods are widely used in empirical research to identify causal effects of a policy. In the local average treatment effect (LATE) framework, the IV estimand identifies the LATE under three main assumptions: random assignment, exclusion restriction, and monotonicity. However, these assumptions are often questionable in many applications, leading some researchers to doubt the causal interpretation of the IV estimand. This paper considers a robust identification of causal parameters in a randomized experiment setting with noncompliance where the standard LATE assumptions could be violated. We discuss identification under two sets of weaker assumptions: random assignment and exclusion restriction (without monotonicity), and random assignment and monotonicity (without exclusion restriction). We derive sharp bounds on some causal parameters under these two sets of relaxed LATE assumptions. Finally, we apply our method to revisit the random information experiment conducted in Bursztyn, Gonz\'alez, and Yanagizawa-Drott (2020) and find that the standard LATE assumptions are jointly incompatible in this application. We then estimate the robust identified sets under the two sets of relaxed assumptions.
New fairness criteria for truncated ballots in multi-winner ranked-choice elections
Authors: Adam Graham-Squire, Matthew I. Jones, David McCune
Date: 2024-08-07 00:00:00
Tasks: Fairness

In real-world elections where voters cast preference ballots, voters often provide only a partial ranking of the candidates. Despite this empirical reality, prior social choice literature frequently analyzes fairness criteria under the assumption that all voters provide a complete ranking of the candidates. We introduce new fairness criteria for multiwinner ranked-choice elections concerning truncated ballots. In particular, we define notions of the independence of losing voters blocs and independence of winning voters blocs, which state that the winning committee of an election should not change when we remove partial ballots which rank only losing candidates, and the winning committee should change in reasonable ways when removing ballots which rank only winning candidates. Of the voting methods we analyze, the Chamberlin-Courant rule performs the best with respect to these criteria, the expanding approvals rule performs the worst, and the method of single transferable vote falls in between.
Firms' Risk Adjustments to Minimum Wage: Financial Leverage and Labor Share Trade-off
Authors: Ying Liang
Date: 2024-08-07 00:00:00

This paper evaluates the impact of the German minimum wage policy on firms' financial leverage. By using a comprehensive firm-establishment-employee linked dataset and a difference-in-differences estimation with firm-level variation in treatment intensity, the analysis shows that the average minimum wage level reduces firms' financial leverage by about 0.5 to 0.9 percentage points, corresponding to 1 to 2 percent of the mean of financial leverage. Further investigation of the mechanism shows that the minimum wage does not lead to significant capital-labor substitution; therefore, the labor share increases. Firms react to the increased labor share by deleveraging. The results suggest that while the minimum wage benefits workers by allocating more earnings to the labor force, it also introduces greater operating risks and encourages conservative financial behavior among firms.
"The Strength of Weak Ties" Varies Across Viral Channels
Authors: Shan Huang, Yuan Yuan, Yi Ji
Date: 2024-08-07 00:00:00

The diffusion of novel information through social networks is essential for dismantling echo chambers and promoting innovation. Our study examines how two major types of viral channels, specifically Direct Messaging (DM) and Broadcasting (BC), impact the well-known "strength of weak ties" in disseminating novel information across social networks. We conducted a large-scale empirical analysis, examining the sharing behavior of 500,000 users over a two-month period on a major social media platform. Our results suggest a greater capacity for DM to transmit novel information compared to BC, although DM typically involves stronger ties. Furthermore, the "strength of weak ties" is only evident in BC, not in DM where weaker ties do not transmit significantly more novel information. Our mechanism analysis indicates that the content selection by both senders and recipients, contingent on tie strength, contributes to the observed differences between these two channels. These findings expand both our understanding of contemporary weak tie theory and our knowledge of how to disseminate novel information in social networks.
The Design and Price of Influence
Authors: Raphael Boleslavsky, Aaron Kolb
Date: 2024-08-07 00:00:00
Tasks: Informativeness

A sender with private preferences would like to influence a receiver's action by providing information through a statistical test. The technology for information production is controlled by a monopolist intermediary, who offers a menu of tests and prices to screen the sender's type. We characterize the intermediary's optimal screening menu and the associated distortions, which may benefit the receiver by increasing test informativeness. Because of these distortions, seemingly unfavorable changes in the prior belief may actually benefit the sender. In extensions, we study (i) a stronger intermediary who can commit to a ``coercive'' test to punish non-participation and (ii) a weaker intermediary who cannot control test design but can charge for access.