pwc-archive/papers-with-abstracts huggingface dataset:
Self-triggered Consensus Control of Multi-agent Systems from Data
Authors: Yifei Li, Xin Wang, Jian Sun, Gang Wang, Jie Chen
Date: 2023-10-19 00:00:00

This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advantages in avoiding continuous monitoring and saving computing and communication resources. However, existing results require the knowledge of system matrices, which are difficult to obtain in real-world settings. To address this challenge, we present a data-driven approach to designing STMs for unknown MASs building upon the model-based solutions. Our approach leverages a system lifting method, which allows us to derive a data-driven representation for the MAS. Subsequently, a data-driven self-triggered consensus control (STC) scheme is designed, which combines a data-driven STM with a state feedback control law. We establish a data-based stability criterion for asymptotic consensus of the closed-loop MAS in terms of linear matrix inequalities, whose solution provides a matrix for the STM as well as a stabilizing controller gain. In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC. Numerical tests are conducted to validate the correctness of the data- and model-based STC approaches. Our data-driven approach demonstrates a superior trade-off between control performance and communication efficiency from finite, noisy data relative to the system identification-based one.
Quantum computing through the lens of control: A tutorial introduction
Authors: Julian Berberich, Daniel Fink
Date: 2023-10-19 00:00:00

Quantum computing is a fascinating interdisciplinary research field that promises to revolutionize computing by efficiently solving previously intractable problems. Recent years have seen tremendous progress on both the experimental realization of quantum computing devices as well as the development and implementation of quantum algorithms. Yet, realizing computational advantages of quantum computers in practice remains a widely open problem due to numerous fundamental challenges. Interestingly, many of these challenges are connected to performance, robustness, scalability, optimization, or feedback, all of which are central concepts in control theory. This paper provides a tutorial introduction to quantum computing from the perspective of control theory. We introduce the mathematical framework of quantum algorithms ranging from basic elements including quantum bits and quantum gates to more advanced concepts such as variational quantum algorithms and quantum errors. The tutorial only requires basic knowledge of linear algebra and, in particular, no prior exposure to quantum physics. Our main goal is to equip readers with the mathematical basics required to understand and possibly solve (control-related) problems in quantum computing. In particular, beyond the tutorial introduction, we provide a list of research challenges in the field of quantum computing and discuss their connections to control.
System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme
Authors: M. Rahmani Dehaghani, Atieh Sahraeidolatkhaneh, Morgan Nilsen, Fredrik Sikström, Pouyan Sajadi, Yifan Tang, G. Gary Wang
Date: 2023-10-18 00:00:00

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, monitoring and controlling the process parameters and signatures during the deposition is crucial to ensure the quality of final part properties and geometries. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-property modeling and control approach to enhance the quality of modeling and control of part properties that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (property) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F_1 and a signature-property model as F_2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F_1). A fully connected artificial neural network is employed to model and predict the final part property, i.e., bead width, based on melt pool signatures (F_2). Finally, the effectiveness and usefulness of the proposed parameter-signature-property modeling is tested and verified by integrating the parameter-signature (F_1) and signature-property (F_2) models in the closed-loop control of the width of the part. Compared with the control loop with only F_1, the proposed method shows clear advantages and bears potential to be applied to control other part properties that cannot be directly measured or monitored in situ.
A Link-Based Flow Model with Turn-Level Queue Transmission and Time-Varying Free-Flow Speed for Urban Road Networks
Authors: Lei Wei, S. Travis Waller, Yu Mei, Peng Chen, Yunpeng Wang, Meng Wang
Date: 2023-10-18 00:00:00

Macroscopic link-based flow models are efficient for simulating flow propagation in urban road networks. Existing link-based flow models described traffic states of a link with two state variables of link inflow and outflow and assumed homogeneous traffic states within a whole link. Consequently, the turn-level queue length change within the link can not be captured, resulting in underrepresented queue spillback. Moreover, a constant link free-flow speed was assumed to formulate models, restricting their applicability in modeling phenomena involving time-varying free-flow speed. This study proposed a new link-based flow model by introducing an additional state variable of link queue inflow and adapting the link outflow to be free-flow speed-dependent. In our model, the vehicle propagation within each link is described by the link inflow, queue inflow, and outflow, which depends on the link free-flow speed changes. A node model is further defined to capture the presence of signal control and potential queue spillback, which estimates the constrained flow propagation between adjacent road segments. Simulation experiments were conducted on a single intersection and a network with consecutive intersections to verify the proposed model performance. Results demonstrate the predictive power of the proposed model in predicting traffic operations of intersections with multiple turning movements and time-varying free-flow speed. Our model outperforms the baseline link-based flow model and preserves the computational tractability property of link-based flow models.
Towards Parsimonious Generative Modeling of RNA Families
Authors: Francesco Calvanese, Camille N. Lambert, Philippe Nghe, Francesco Zamponi, Martin Weigt
Date: 2023-10-19 00:00:00

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately $\mathbf{10^{39}}$ functional nucleotide sequences. While huge compared to the known $< \mathbf{4,000}$ natural sequences, this number represents only a tiny fraction of the vast pool of nearly $\mathbf{10^{82}}$ possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors
Authors: Ali Tavasoli, Heman Shakeri
Date: 2023-09-07 00:00:00
Tasks: Interpretable Machine Learning

This paper examines the use of operator-theoretic approaches to the analysis of chaotic systems through the lens of their unstable periodic orbits (UPOs). Our approach involves three data-driven steps for detecting, identifying, and stabilizing UPOs. We demonstrate the use of kernel integral operators within delay coordinates as an innovative method for UPO detection. For identifying the dynamic behavior associated with each individual UPO, we utilize the Koopman operator to present the dynamics as linear equations in the space of Koopman eigenfunctions. This allows for characterizing the chaotic attractor by investigating its principal dynamical modes across varying UPOs. We extend this methodology into an interpretable machine learning framework aimed at stabilizing strange attractors on their UPOs. To illustrate the efficacy of our approach, we apply it to the Lorenz attractor as a case study.
Boolean Networks as Predictive Models of Emergent Biological Behaviors
Authors: Jordan C. Rozum, Colin Campbell, Eli Newby, Fatemeh Sadat Fatemi Nasrollahi, Reka Albert
Date: 2023-10-19 00:00:00

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.
The Social Cost of Carbon
Authors: Richard S. J. Tol
Date: 2023-10-19 00:00:00

The social cost of carbon is the damage avoided by slightly reducing carbon dioxide emissions. It is a measure of the desired intensity of climate policy. The social cost of carbon is highly uncertain because of the long and complex cause-effect chain, and because it quantifies and aggregates impacts over a long period of time, affecting all people in a wide range of possible futures. Recent estimates are around $\$$80/tCO$_2$.
Nonparametric Regression with Dyadic Data
Authors: Brice Romuald Gueyap Kounga
Date: 2023-10-19 00:00:00
Tasks: regression

This paper studies the identification and estimation of a nonparametric nonseparable dyadic model where the structural function and the distribution of the unobservable random terms are assumed to be unknown. The identification and the estimation of the distribution of the unobservable random term are also proposed. I assume that the structural function is continuous and strictly increasing in the unobservable heterogeneity. I propose suitable normalization for the identification by allowing the structural function to have some desirable properties such as homogeneity of degree one in the unobservable random term and some of its observables. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a Monte-Carlo simulation are assessed.
Price dispersion across online platforms: Evidence from hotel room prices in London (UK)
Authors: Debashrita Mohapatra, Debi Prasad Mohapatra, Ram Sewak Dubey
Date: 2023-10-18 00:00:00

This paper studies the widespread price dispersion of homogeneous products across different online platforms, even when consumers can easily access price information from comparison websites. We collect data for the 200 most popular hotels in London (UK) and document that prices vary widely across booking sites while making reservations for a hotel room. Additionally, we find that prices listed across different platforms tend to converge as the booking date gets closer to the date of stay. However, the price dispersion persists until the date of stay, implying that the "law of one price" does not hold. We present a simple theoretical model to explain this and show that in the presence of aggregate demand uncertainty and capacity constraints, price dispersion could exist even when products are homogeneous, consumers are homogeneous, all agents have perfect information about the market structure, and consumers face no search costs to acquire information about the products. Our theoretical intuition and robust empirical evidence provide additional insights into price dispersion across online platforms in different institutional settings. Our study complements the existing literature that relies on consumer search costs to explain the price dispersion phenomenon.
Peer Effects in Consideration and Preferences
Authors: Nail Kashaev, Natalia Lazzati, Ruli Xiao
Date: 2023-10-18 00:00:00
Tasks: counterfactual

We develop a general model of discrete choice that incorporates peer effects in preferences and consideration sets. We characterize the equilibrium behavior and establish conditions under which all parts of the model can be recovered from a sequence of choices. We allow peers to affect only preferences, only consideration, or both. We show that these peer-effect mechanisms have different behavioral implications in the data. This allows us to recover the set and the type of connections between the agents in the network. We then use this information to recover the preferences and the consideration mechanisms of each agent. These nonparametric identification results allow for general forms of heterogeneity across agents and do not rely on the variation of either exogenous covariates or the set of available options (menus). We apply our results to model expansion decisions by coffee chains and find evidence of limited consideration. We simulate counterfactual predictions and show how limited consideration slows down competition.
A note on the logical inconsistency of the Hotelling Rule: A Revisit from the System's Analysis Perspective
Authors: Nikolay Khabarov, Alexey Smirnov, Michael Obersteiner
Date: 2023-10-19 00:00:00

The "Hotelling rule" (HR) called to be "the fundamental principle of the economics of exhaustible resources" has a logical deficiency which was never paid a sufficient attention to. This deficiency should be taken into account before attempting to explain discrepancies between the price prediction provided by the HR and historically observed prices. Our analysis is focused on the HR in its original form, we do not touch upon other aspects such as varying extraction costs and other ways of upgrading the original model underlying the Hotelling rule. We conclude that HR can not be derived from the simple models as it was claimed, therefore it should be understood as an assumption on its own, and not as a rule derived from other more basic assumptions.
Walraswap: a solution to uniform price batch auctions
Authors: Sergio A. Yuhjtman
Date: 2023-10-18 00:00:00

Consider a finite set of trade orders and automated market makers (AMMs) at some state. We propose a solution to the problem of finding an equilibrium price vector to execute all the orders jointly with corresponding optimal AMMs swaps. The solution is based on Brouwer's fixed-point theorem. We discuss computational aspects relevant for realistic situations in public blockchain activity.
Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function
Authors: HsinYu Chang, Yicheng Hsu, Mingsian R. Bai
Date: 2023-10-19 00:00:00
Tasks: Speech Enhancement

Recent research advances in deep neural network (DNN)-based beamformers have shown great promise for speech enhancement under adverse acoustic conditions. Different network architectures and input features have been explored in estimating beamforming weights. In this paper, we propose a deep beamformer based on an efficient convolutional recurrent network (CRN) trained with a novel ARray RespOnse-aWare (ARROW) loss function. The ARROW loss exploits the array responses of the target and interferer by using the ground truth relative transfer functions (RTFs). The DNN-based beamforming system, trained with ARROW loss through supervised learning, is able to perform speech enhancement and speaker localization jointly. Experimental results have shown that the proposed deep beamformer, trained with the linearly weighted scale-invariant source-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superior performance in speech enhancement and speaker localization compared to two baselines.
On Feature Importance and Interpretability of Speaker Representations
Authors: Frederik Rautenberg, Michael Kuhlmann, Jana Wiechmann, Fritz Seebauer, Petra Wagner, Reinhold Haeb-Umbach
Date: 2023-10-19 00:00:00
Tasks: Disentanglement, Feature Importance

Unsupervised speech disentanglement aims at separating fast varying from slowly varying components of a speech signal. In this contribution, we take a closer look at the embedding vector representing the slowly varying signal components, commonly named the speaker embedding vector. We ask, which properties of a speaker's voice are captured and investigate to which extent do individual embedding vector components sign responsible for them, using the concept of Shapley values. Our findings show that certain speaker-specific acoustic-phonetic properties can be fairly well predicted from the speaker embedding, while the investigated more abstract voice quality features cannot.
A New Time Series Similarity Measure and Its Smart Grid Applications
Authors: Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Andrew J. Black, Jon A. R. Liisberg, Julian Lemos-Vinasco
Date: 2023-10-19 00:00:00
Tasks: Anomaly Detection, Dynamic Time Warping, Scheduling, Time Series

Many smart grid applications involve data mining, clustering, classification, identification, and anomaly detection, among others. These applications primarily depend on the measurement of similarity, which is the distance between different time series or subsequences of a time series. The commonly used time series distance measures, namely Euclidean Distance (ED) and Dynamic Time Warping (DTW), do not quantify the flexible nature of electricity usage data in terms of temporal dynamics. As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e.g., demand response and load profiling. This paper introduces a novel distance measure to compare electricity usage patterns. The method consists of two phases that quantify the effort required to reshape one time series into another, considering both amplitude and temporal changes. The proposed method is evaluated against ED and DTW using real-world data in three smart grid applications. Overall, the proposed measure outperforms ED and DTW in accurately identifying the best load scheduling strategy, anomalous days with irregular electricity usage, and determining electricity users' behind-the-meter (BTM) equipment.
Product of Gaussian Mixture Diffusion Models
Authors: Martin Zach, Erich Kobler, Antonin Chambolle, Thomas Pock
Date: 2023-10-19 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

In this work we tackle the problem of estimating the density $ f_X $ of a random variable $ X $ by successive smoothing, such that the smoothed random variable $ Y $ fulfills the diffusion partial differential equation $ (\partial_t - \Delta_1)f_Y(\,\cdot\,, t) = 0 $ with initial condition $ f_Y(\,\cdot\,, 0) = f_X $. We propose a product-of-experts-type model utilizing Gaussian mixture experts and study configurations that admit an analytic expression for $ f_Y (\,\cdot\,, t) $. In particular, with a focus on image processing, we derive conditions for models acting on filter-, wavelet-, and shearlet responses. Our construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show numerical results for image denoising where our models are competitive while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our models can be used for reliable noise level estimation, allowing blind denoising of images corrupted by heteroscedastic noise.
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution
Authors: Jinglei Hao, Wukai Li, Binglu Wang, Shunzhou Wang, Yuting Lu, Ning li, Yongqiang Zhao
Date: 2023-10-19 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Transformer Layers (SPTLs) for scale-aware low-resolution feature learning and Context-aware Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. MBT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that MBT obtains state-of-the-art results compared to other leading methods.
Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework
Authors: Yizhou Peng, Songjie Yang, Wanting Lyu, Ya Li, Hongjun He, Zhongpei Zhang, Chadi Assi
Date: 2023-10-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) system is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users a sensing target. Based on the theory of mutual-information (MI), communication MI and sensing MI rate are utilized as the performance metrics under the presence of clutters. In particular, we propose an novel MI-based WMMSE-ISAC method by developing a unique transceiver design mechanism to maximize the weighted sensing and communication sum-rate of this system. Such a maximization process is achieved by utilizing the classical method -- WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated.