pwc-archive/papers-with-abstracts huggingface dataset:
Specifying and Analyzing Networked and Layered Control Systems Operating on Multiple Clocks
Authors: Inigo Incer, Noel Csomay-Shanklin, Aaron Ames, Richard M. Murray
Date: 2024-02-18 00:00:00
Tasks: Model Predictive Control

We consider the problem of reasoning about networked and layered control systems using assume-guarantee specifications. As these systems are formed by the interconnection of components that operate under various clocks, we introduce a new logic, Multiclock Logic (MCL), to be able to express the requirements of components form the point of view of their local clocks. Specifying components locally promotes independent design and component reuse. We carry out a contract-based analysis of a control system implemented via two control algorithms (model predictive control and feedback linearization) running on their own processors and clocks. Then we implement each of the contracts to build a system. The system performs as desired when the requirements derived from our system-level analysis are respected. Violating the constraints required by the contract-based analysis of the system leads to error.
Iterative Linear Quadratic Regulator With Variational Equation-Based Discretization
Authors: Katsuya Shigematsu, Hikaru Hoshino, Eiko Furutani
Date: 2024-02-18 00:00:00

This paper discusses discretization methods for implementing nonlinear model predictive controllers using Iterative Linear Quadratic Regulator (ILQR). Finite-difference approximations are mostly used to derive a discrete-time state equation from the original continuous-time model. However, the timestep of the discretization is sometimes restricted to be small to suppress the approximation error. In this paper, we propose to use the variational equation for deriving linearizations of the discretized system required in ILQR algorithms, which allows accurate computation regardless of the timestep. Numerical simulations of the swing-up control of an inverted pendulum demonstrate the effectiveness of this method. By the relaxing stringent requirement for the size of the timestep, the use of the variational equation can improve control performance by increasing the number of ILQR iterations possible at each timestep in the realtime computation.
Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency (Extended Version)
Authors: Algo Carè, Erik Weyer, Balázs Cs. Csáji, Marco C. Campi
Date: 2024-02-18 00:00:00

Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence regions for the unknown system parameters. In this paper, we study SPS for ARX systems, and establish that the confidence regions are guaranteed to include the true model parameter with exact, user-chosen, probability under mild statistical assumptions, a property that holds true for any finite number of observed input-output data. Furthermore, we prove the strong consistency of the method, that is, as the number of data points increases, the confidence region gets smaller and smaller and will asymptotically almost surely exclude any parameter value different from the true one. In addition, we also show that, asymptotically, the SPS region is included in an ellipsoid which is marginally larger than the confidence ellipsoid obtained from the asymptotic theory of system identification. The results are theoretically proven and illustrated in a simulation example.
Exponential Cluster Synchronization in Fast Switching Network Topologies: A Pinning Control Approach with Necessary and Sufficient Conditions
Authors: Ku Du, Yu Kang
Date: 2024-02-18 00:00:00

This research investigates the intricate domain of synchronization problem among multiple agents operating within a dynamic fast switching network topology. We concentrate on cluster synchronization within coupled linear system under pinning control, providing both necessary and sufficient conditions. As a pivotal aspect, this paper aim to president the weakest possible conditions to make the coupled linear system realize cluster synchronization exponentially. Within the context of fast switching framework, we initially examine the necessary conditions, commencing with the transformation of the consensus problem into a stability problem, introducing a new variable to make the coupled system achieve cluster synchronization if the system is controllable; communication topology switching fast enough and the coupling strength should be sufficiently robust. Then, by using the Lyapunov theorem, we also present that the state matrix controllable is necessary for cluster synchronization. Furthermore, this paper culminating in the incorporation of contraction theory and an invariant manifold, demonstrating that the switching topology has an average is imperative for achieving cluster synchronization. Finally, we introduce three simulations to validate the efficacy of the proposed approach.
A Fisher Information based Receding Horizon Control Method for Signal Strength Model Estimation
Authors: Yancheng Zhu, Sean B. Andersson
Date: 2024-02-18 00:00:00

This paper considers the problem of localizing a set of nodes in a wireless sensor network when both their positions and the parameters of the communication model are unknown. We assume that a single agent moves through the environment, taking measurements of the Received Signal Strength (RSS), and seek a controller that optimizes a performance metric based on the Fisher Information Matrix (FIM). We develop a receding horizon (RH) approach that alternates between estimating the parameter values (using a maximum likelihood estimator) and determining where to move so as to maximally inform the estimation problem. The receding horizon controller solves a multi-stage look ahead problem to determine the next control to be applied, executes the move, collects the next measurement, and then re-estimates the parameters before repeating the sequence. We consider both a Dynamic Programming (DP) approach to solving the optimal control problem at each step, and a simplified heuristic based on a pruning algorithm that significantly reduces the computational complexity. We also consider a modified cost function that seeks to balance the information acquired about each of the parameters to ensure the controller does not focus on a single value in its optimization. These approaches are compared against two baselines, one based on a purely random trajectory and one on a greedy control solution. The simulations indicate our RH schemes outperform the baselines, while the pruning algorithm produces significant reductions in computation time with little effect on overall performance.
Federated Reinforcement Learning for Uplink Centric Broadband Communication Optimization over Unlicensed Spectrum
Authors: Hui Zhou, Yansha Deng
Date: 2024-02-18 00:00:00

To provide Uplink Centric Broadband Communication (UCBC), New Radio Unlicensed (NR-U) network has been standardized to exploit the unlicensed spectrum using Listen Before Talk (LBT) scheme to fairly coexist with the incumbent Wireless Fidelity (WiFi) network. Existing access schemes over unlicensed spectrum are required to perform Clear Channel Assessment (CCA) before transmissions, where fixed Energy Detection (ED) thresholds are adopted to identify the channel as idle or busy. However, fixed ED thresholds setting prevents devices from accessing the channel effectively and efficiently, which leads to the hidden node (HN) and exposed node (EN) problems. In this paper, we first develop a centralized double Deep Q-Network (DDQN) algorithm to optimize the uplink system throughput, where the agent is deployed at the central server to dynamically adjust the ED thresholds for NR-U and WiFi networks. Considering that heterogeneous NR-U and WiFi networks, in practice, cannot share the raw data with the central server directly, we then develop a federated DDQN algorithm, where two agents are deployed in the NR-U and WiFi networks, respectively. Our results have shown that the uplink system throughput increases by over 100%, where cell throughput of NR-U network rises by 150%, and cell throughput of WiFi network decreases by 30%. To guarantee the cell throughput of WiFi network, we redesign the reward function to punish the agent when the cell throughput of WiFi network is below the threshold, and our revised design can still provide over 50% uplink system throughput gain.
Balanced Truncation of Linear Systems with Quadratic Outputs in Limited Time and Frequency Intervals
Authors: Qiu-Yan Song, Umair Zulfiqar, Zhi-Hua Xiao, Mohammad Monir Uddin, Victor Sreeram
Date: 2024-02-18 00:00:00

Model order reduction involves constructing a reduced-order approximation of a high-order model while retaining its essential characteristics. This reduced-order model serves as a substitute for the original one in various applications such as simulation, analysis, and design. Often, there's a need to maintain high accuracy within a specific time or frequency interval, while errors beyond this limit can be tolerated. This paper addresses time-limited and frequency-limited model order reduction scenarios for linear systems with quadratic outputs, by generalizing the recently introduced structure-preserving balanced truncation algorithm. To that end, limited interval system Gramians are defined, and the corresponding generalized Lyapunov equations governing their computation are derived. Additionally, low-rank solutions for these equations are investigated. Next, balanced truncation algorithms are proposed for time-limited and frequency-limited scenarios, each utilizing its corresponding limited-interval system Gramians. The proposed algorithms ensure accurate results within specified time and frequency intervals while preserving the quadratic-output structure. Two benchmark numerical examples are presented to demonstrate the effectiveness of the algorithms, showcasing their ability to achieve superior accuracy within the desired time or frequency interval.
Generative AI and Process Systems Engineering: The Next Frontier
Authors: Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, Fengqi You
Date: 2024-02-15 00:00:00
Tasks: Decision Making, Image Generation

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
The dynamics of neural codes in biological and artificial neural networks
Authors: Guillermo B. Morales
Date: 2024-02-20 00:00:00

Advancing our knowledge of how the brain processes information remains a key challenge in neuroscience. This thesis combines three different approaches to the study of the dynamics of neural networks and their encoding representations: a computational approach, that builds upon basic biological features of neurons and their networks to construct effective models that can simulate their structure and dynamics; a machine-learning approach, which draws a parallel with the functional capabilities of brain networks, allowing us to infer the dynamical and encoding properties required to solve certain input-processing tasks; and a final, theoretical treatment, which will take us into the fascinating hypothesis of the "critical" brain as the mathematical foundation that can explain the emergent collective properties arising from the interactions of millions of neurons. Hand in hand with physics, we venture into the realm of neuroscience to explain the existence of quasi-universal scaling properties across brain regions, setting out to quantify the distance of their dynamics from a critical point. Next, we move into the grounds of artificial intelligence, where the very same theory of critical phenomena will prove very useful for explaining the effects of biologically-inspired plasticity rules in the forecasting ability of Reservoir Computers. Halfway into our journey, we explore the concept of neural representations of external stimuli, unveiling a surprising link between the dynamical regime of neural networks and the optimal topological properties of such representation manifolds. The thesis ends with the singular problem of representational drift in the process of odor encoding carried out by the olfactory cortex, uncovering the potential synaptic plasticity mechanisms that could explain this recently observed phenomenon.
Non-contact photoacoustic imaging with a silicon photonics-based Laser Doppler Vibrometer
Authors: Emiel Dieussaert, Roel Baets, Hilde Jans, Xavier Rottenberg, Yanlu Li
Date: 2024-02-14 00:00:00
Tasks: Contact Detection

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive and bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical applications. While other non-contact detection methods can be bulky, in this paper, we demonstrate compact and contactless detection of photoacoustic signals on silicone samples embedded with ink-filled channels, mimicking tissue with blood-carrying veins. A silicon photonics-based Laser Doppler Vibrometer (LDV) detects the acoustic waves excited by a compact pulsed laser diode. By scanning the LDV beam over the surface of the sample, 2D photoacoustic images were reconstructed of the sample. Photoacoustic signals with absorption coefficients within the physiological range were detected by this setup.
Low-Dose CT Reconstruction Using Dataset-free Learning
Authors: Feng Wang, Renfang Wang, Hong Qiu
Date: 2024-02-17 00:00:00
Tasks: CT Reconstruction

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neural network training, which limits their practical application in LDCT imaging. In this paper, we propose an unsupervised and training data-free learning reconstruction method for LDCT imaging that avoids the requirement for training data. The proposed method is a post-processing technique that aims to enhance the initial low-quality reconstruction results, and it reconstructs the high-quality images by neural work training that minimizes the $\ell_1$-norm distance between the CT measurements and their corresponding simulated sinogram data, as well as the total variation (TV) value of the reconstructed image. Moreover, the proposed method does not require to set the weights for both the data fidelity term and the plenty term. Experimental results on the AAPM challenge data and LoDoPab-CT data demonstrate that the proposed method is able to effectively suppress the noise and preserve the tiny structures. Also, these results demonstrate the rapid convergence and low computational cost of the proposed method. The source code is available at \url{https://github.com/linfengyu77/IRLDCT}.
Postural adjustments preceding string release in trained archers
Authors: Andrian Kuch, Romain Tisserand, François Durand, Tony Monnet, Jean-François Debril
Date: 2024-02-14 00:00:00

Optimal postural stability is required to perform in archery. Since the dynamic consequences of the string release may disturb the archer's postural equilibrium, they should have integrated them in their motor program to optimize postural stability. This study aimed to characterize the postural strategy archers use to limit the potentially detrimental impact of the bow release on their postural stability and identify characteristics that may explain a better performance. Six elite and seven sub-elite archers performed a series of 18 shots at 70 meters, standing on two force plates. Postural stability indicators were computed during the aiming and the shooting phase using the trajectory of the center of pressure. Two postural strategies were defined, as whether they were triggered before (early) or after (late) the string release time. Both groups used anticipated postural adjustments, but elite archers triggered them before the string release more often and sooner. Scores differed between the two groups, but no differences were found between early and late shots. Trained archers seem to have finely integrated the dynamic consequences of their bow motion, triggering anticipated postural adjustments prior to the string release. However, it remains unclear whether this anticipation can positively influence the performance outcome.
Interbank network reconstruction enforcing density and reciprocity
Authors: Valentina Macchiati, Piero Mazzarisi, Diego Garlaschelli
Date: 2024-02-17 00:00:00

Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the statistics of higher-order cycles is strongly constrained by that of the shortest ones, i.e. by the amount of dyads with reciprocated links. First, we provide a detailed analysis of link reciprocity on the e-MID dataset of Italian banks, finding that correlations between reciprocal links systematically increase with the temporal resolution, typically changing from negative to positive around a timescale of up to 50 days. Then, we propose a new network reconstruction method capable of enforcing, only from the knowledge of aggregate interbank assets and liabilities, both a desired sparsity and a desired link reciprocity. We confirm that the addition of reciprocity dramatically improves the prediction of several structural and spectral network properties, including the largest real eigenvalue and the eccentricity of the elliptical distribution of the other eigenvalues in the complex plane. These results illustrate the importance of correctly addressing the temporal resolution and the resulting level of reciprocity in the reconstruction of financial networks.
Bridging Methodologies: Angrist and Imbens' Contributions to Causal Identification
Authors: Lucas Girard, Yannick Guyonvarch
Date: 2024-02-20 00:00:00
Tasks: Articles, Causal Identification

In the 1990s, Joshua Angrist and Guido Imbens studied the causal interpretation of Instrumental Variable estimates (a widespread methodology in economics) through the lens of potential outcomes (a classical framework to formalize causality in statistics). Bridging a gap between those two strands of literature, they stress the importance of treatment effect heterogeneity and show that, under defendable assumptions in various applications, this method recovers an average causal effect for a specific subpopulation of individuals whose treatment is affected by the instrument. They were awarded the Nobel Prize primarily for this Local Average Treatment Effect (LATE). The first part of this article presents that methodological contribution in-depth: the origination in earlier applied articles, the different identification results and extensions, and related debates on the relevance of LATEs for public policy decisions. The second part reviews the main contributions of the authors beyond the LATE. J. Angrist has pursued the search for informative and varied empirical research designs in several fields, particularly in education. G. Imbens has complemented the toolbox for treatment effect estimation in many ways, notably through propensity score reweighting, matching, and, more recently, adapting machine learning procedures.
Extending the Scope of Inference About Predictive Ability to Machine Learning Methods
Authors: Juan Carlos Escanciano, Ricardo Parra
Date: 2024-02-20 00:00:00
Tasks: Time Series, Uncertainty Quantification, valid

The use of machine learning methods for predictive purposes has increased dramatically over the past two decades, but uncertainty quantification for predictive comparisons remains elusive. This paper addresses this gap by extending the classic inference theory for predictive ability in time series to modern machine learners, such as the Lasso or Deep Learning. We investigate under which conditions such extensions are possible. For standard out-of-sample asymptotic inference to be valid with machine learning, two key properties must hold: (I) a zero-mean condition for the score of the prediction loss function and (ii) a "fast rate" of convergence for the machine learner. Absent any of these conditions, the estimation risk may be unbounded, and inferences invalid and very sensitive to sample splitting. For accurate inferences, we recommend an 80%-20% training-test splitting rule. We illustrate the wide applicability of our results with three applications: high-dimensional time series regressions with the Lasso, Deep learning for binary outcomes, and a new out-of-sample test for the Martingale Difference Hypothesis (MDH). The theoretical results are supported by extensive Monte Carlo simulations and an empirical application evaluating the MDH of some major exchange rates at daily and higher frequencies.
Inference on LATEs with covariates
Authors: Tom Boot, Didier Nibbering
Date: 2024-02-20 00:00:00
Tasks: valid

In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification, without making parametric assumptions on how available covariates enter the model. In practice, TSLS is severely biased as saturation leads to a large number of control dummies and an equally large number of, arguably weak, instruments. This paper derives asymptotically valid tests and confidence intervals for the weighted average of LATEs that is targeted, yet missed by saturated TSLS. The proposed inference procedure is robust to unobserved treatment effect heterogeneity, covariates with rich support, and weak identification. We find LATEs statistically significantly different from zero in applications in criminology, finance, health, and education.