pwc-archive/papers-with-abstracts huggingface dataset:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Authors: Jing Dong, Baoxiang Wang, YaoLiang Yu
Date: 2024-04-04 00:00:00

In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient exploration and recursive gradient estimation, which provably converges to the Nash equilibrium while attaining sublinear regret for each individual player. Our algorithm simultaneously achieves a Nash regret and a regret bound of $O(T^{4/5})$ for potential games, which matches the best available result, without using additional projection steps. Through carefully balancing the reuse of past samples and exploration of new samples, we then extend the results to Markov potential games and improve the best available Nash regret from $O(T^{5/6})$ to $O(T^{4/5})$. Moreover, our algorithm requires no knowledge of the game, such as the distribution mismatch coefficient, which provides more flexibility in its practical implementation. Experimental results corroborate our theoretical findings and underscore the practical effectiveness of our method.
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks
Authors: Xenofon Karakonstantis, Efren Fernandez-Grande, Peter Gerstoft
Date: 2024-04-10 00:00:00
Tasks: Bayesian Inference, Computational Efficiency

In this study, we introduce a method for estimating sound fields in reverberant environments using a conditional invertible neural network (CINN). Sound field reconstruction can be hindered by experimental errors, limited spatial data, model mismatches, and long inference times, leading to potentially flawed and prolonged characterizations. Further, the complexity of managing inherent uncertainties often escalates computational demands or is neglected in models. Our approach seeks to balance accuracy and computational efficiency, while incorporating uncertainty estimates to tailor reconstructions to specific needs. By training a CINN with Monte Carlo simulations of random wave fields, our method reduces the dependency on extensive datasets and enables inference from sparse experimental data. The CINN proves versatile at reconstructing Room Impulse Responses (RIRs), by acting either as a likelihood model for maximum a posteriori estimation or as an approximate posterior distribution through amortized Bayesian inference. Compared to traditional Bayesian methods, the CINN achieves similar accuracy with greater efficiency and without requiring its adaptation to distinct sound field conditions.
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions
Authors: Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah
Date: 2024-04-10 00:00:00
Tasks: Emotion Recognition, Keyword Spotting, Language Identification

There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems. However, there is a dearth of analyses of what is actually learnt and the relative importance of training the different components of the front-end. In this paper, we investigate this question on keyword spotting, speech-based emotion recognition and language identification tasks and find that the filters for spectral decomposition and the low pass filter used to estimate spectral energy variations exhibit no learning and the per-channel energy normalisation (PCEN) is the key component that is learnt. Following this, we explore the potential of adapting only the PCEN layer with a small amount of noisy data to enable it to learn appropriate dynamic range compression that better suits the noise conditions. This in turn enables a system trained on clean speech to work more accurately on noisy test data as demonstrated by the experimental results reported in this paper.
Dynamic Mode Decomposition with Non-uniform Sampling
Authors: Ramachandran Anantharaman, Alexandre Mauroy
Date: 2024-04-10 00:00:00

Dynamic Mode Decomposition (DMD) and its extensions (EDMD) have been at the forefront of data-based approaches to Koopman operators. Most (E)DMD algorithms assume that the entire state is sampled at a uniform sampling rate. In this paper, we provide an algorithm where the entire state is not uniformly sampled, with individual components of the states measured at individual (but known) sampling rates. We propose a two-step DMD algorithm where the first step performs Hankel DMD on individual state components to estimate them at specified time instants. With the entire state reconstructed at the same time instants, we compute the (E)DMD for the system with the estimated data in the second step.
Analytical Formula for Calculations of Armour Losses in Three-Core Power Cables
Authors: Marius Hatlo, Martin Hovde
Date: 2024-04-10 00:00:00

Over the past decade, significant progress has been made in the field of loss and rating calculations for armoured three-core cables. This development was prompted by an industry realization that the applicable international standards often overestimate losses, leading to unnecessarily bulky and more expensive cables. Starting with first-principles, this paper presents an accurate analytical formula for armour losses in three-core cables. The formula has undergone rigorous validation against 3D Finite Element Analysis (FEA) and demonstrate excellent accuracy. In the specific cases examined, the largest deviation from FEA results in terms of armour loss is approximately 2.4 percent for fully armoured cables. Although this study specifically focuses on armour losses, it establishes the groundwork for precise loss calculations in armoured three-core cables, including the conductor and screen losses. And the work presented here formed the basis for the complete loss calculations presented in the CIGRE Technical Brochure 908.
Spatiotemporal Analysis of Shared Situation Awareness among Connected Vehicles
Authors: Seungmo Kim
Date: 2024-04-10 00:00:00

Shared situation awareness (SSA) has been garnering explosive interest in various applications for intelligent transportation systems (ITS). In addition, the delay-constrained nature of supporting vehicular networks makes it critical to precisely analyze the performance of a SSA procedure. Extending the relevant literature, this paper provides an analysis framework that evaluates the performance of SSA in spatial and temporal aspects simultaneously. Specifically, this paper provides a closed-form probability distribution for the length of time taken for constitution of a SSA among a group of connected vehicles. This paper extends the calculation to investigation of feasibility of SSA in supporting various types of safety messages defined by the SAE J2735.
Complementarity-constrained predictive control for efficient gas-balanced hybrid power systems
Authors: Kiet Tuan Hoang, Brage Rugstad Knudsen, Lars Struen Imsland
Date: 2024-04-10 00:00:00

Controlling gas turbines (GTs) efficiently is vital as GTs are used to balance power in onshore/offshore hybrid power systems with variable renewable energy and energy storage. However, predictive control of GTs is non-trivial when formulated as a dynamic optimisation problem due to the semi-continuous operating regions of GTs, which must be included to ensure complete combustion and high fuel efficiency. This paper studies two approaches for handling the semi-continuous operating regions of GTs in hybrid power systems through predictive control, dynamic optimisation, and complementarity constraints. The proposed solutions are qualitatively investigated and compared with baseline controllers in a case study involving GTs, offshore wind, and batteries. While one of the baseline controllers considers fuel efficiency, it employs a continuous formulation, which results in lower efficiency than the two proposed approaches as it does not account for the semi-continuous operating regions of each GT.
Synchronization Conditions for Nonlinear Oscillator Networks
Authors: Sanjeev Kumar Pandey, Shaunak Sen, Indra Narayan Kar
Date: 2024-04-10 00:00:00

Understanding conditions for the synchronization of a network of interconnected oscillators is a challenging problem. Typically, only sufficient conditions are reported for the synchronization problem. Here, we adopted the Lyapunov-Floquet theory and the Master Stability Function approach in order to derive the synchronization conditions for a set of coupled nonlinear oscillators. We found that the positivity of the coupling constant is a necessary and sufficient condition for synchronizing linearly full-state coupled identical oscillators. Moreover, in the case of partial state coupling, the asymptotic convergence of volume in state space is ensured by a positive coupling constant. The numerical calculation of the Master Stability Function for a benchmark two-dimensional oscillator validates the synchronization corresponding to the positive coupling. The results are illustrated using numerical simulations and experimentation on benchmark oscillators.
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
Authors: Xiaojie Li, Song Bo, Xuewen Zhang, Yan Qin, Xunyuan Yin
Date: 2024-04-10 00:00:00
Tasks: Chemical Process, State Estimation

In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, both process modeling and state estimation design are addressed within a distributed framework. By leveraging the Koopman operator concept, a parallel subsystem modeling approach is proposed to establish interactive linear subsystem process models in higher-dimensional subspaces, each of which correlates with the original nonlinear subspace of the corresponding process subsystem via a nonlinear mapping. The data-driven linear subsystem models can be used to collaboratively characterize and predict the dynamical behaviors of the entire nonlinear process. Based on the established subsystem models, local state estimators that can explicitly handle process operation constraints are designed using moving horizon estimation. The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner. The proposed framework is applied to a chemical process and an agro-hydrological process to illustrate its effectiveness and applicability. Good open-loop predictability of the linear subsystem models is confirmed, and accurate estimates of the process states are obtained without requiring a first-principles process model.
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
Authors: Xiaojie Li, Adrian Wing-Keung Law, Xunyuan Yin
Date: 2024-04-10 00:00:00
Tasks: Chemical Process, State Estimation

In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formulate a linear full-information estimation design within a distributed framework as the basis for developing our approach. Second, the analytical solution to the local optimization problems associated with the formulated distributed full-information design is established, in the form of a recursive distributed Kalman filter algorithm. Then, the linear distributed Kalman filter is extended to the nonlinear context by incorporating successive linearization of nonlinear subsystem models, and the proposed distributed extended Kalman filter approach is formulated. We conduct rigorous analysis and prove the stability of the estimation error dynamics provided by the proposed method for general nonlinear processes consisting of interconnected subsystems. A chemical process example is used to illustrate the effectiveness of the proposed method and to justify the validity of the theoretical findings. In addition, the proposed method is applied to a wastewater treatment process for estimating the full state of the process with 145 state variables.
Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
Authors: Xiaojie Li, Song Bo, Yan Qin, Xunyuan Yin
Date: 2024-04-10 00:00:00
Tasks: State Estimation

In this paper, partition-based distributed state estimation of general linear systems is considered. A distributed moving horizon state estimation scheme is developed via decomposing the entire system model into subsystem models and partitioning the global objective function of centralized moving horizon estimation (MHE) into local objective functions. The subsystem estimators of the distributed scheme that are required to be executed iteratively within each sampling period are designed based on MHE. Two distributed MHE algorithms are proposed to handle the unconstrained case and the case when hard constraints on states and disturbances, respectively. Sufficient conditions on the convergence of the estimates and the stability of the estimation error dynamics for the entire system are derived for both cases. A benchmark reactor-separator process example is introduced to illustrate the proposed distributed state estimation approach.
Dual Ensemble Kalman Filter for Stochastic Optimal Control
Authors: Anant A. Joshi, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn
Date: 2024-04-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this paper, stochastic optimal control problems in continuous time and space are considered. In recent years, such problems have received renewed attention from the lens of reinforcement learning (RL) which is also one of our motivation. The main contribution is a simulation-based algorithm -- dual ensemble Kalman filter (EnKF) -- to numerically approximate the solution of these problems. The paper extends our previous work where the dual EnKF was applied in deterministic settings of the problem. The theoretical results and algorithms are illustrated with numerical experiments.
Non-equilibrium microbial dynamics unveil a new macroecological pattern beyond Taylor's law
Authors: José Camacho-Mateu, Aniello Lampo, Saúl Ares, José A. Cuesta
Date: 2024-04-10 00:00:00
Tasks: Relation

We introduce a comprehensive analytical benchmark, relying on Fokker-Planck formalism, to study microbial dynamics in presence of both biotic and abiotic forces. In equilibrium, we observe a balance between the two kinds of forces, leading to no correlations between species abundances. This implies that real microbiomes, where correlations have been observed, operate out of equilibrium. Therefore, we analyze non-equilibrium dynamics, presenting an ansatz for an approximate solution that embodies the complex interplay of forces in the system. This solution is consistent with Taylor's law as a coarse-grained approximation of the relation between species abundance and variance, but implies subtler effects, predicting unobserved structure beyond Taylor's law. Motivated by this theoretical prediction, we refine the analysis of existing metagenomic data, unveiling a novel universal macroecological pattern. Finally, we speculate on the physical origin of Taylor's law: building upon an analogy with Brownian motion theory, we propose that Taylor's law emerges as a Fluctuation-Growth relation resulting from equipartition of environmental resources among microbial species.
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography
Authors: Yutian Zhong, XiaoMing Zhang, Zongxin Mo, Shuangyang Zhang, Wufan Chen, Li Qi
Date: 2024-04-10 00:00:00
Tasks: Image Reconstruction, Self-Supervised Learning

Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer that, when switching excitation wavelengths, simultaneously rotates and translates. This creates a spiral scanning pattern with multispectral angle-interlaced sampling. To solve the highly ill-conditioned image reconstruction problem, we propose a self-supervised learning method that is able to introduce structural information shared during spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT system and conduct in vivo animal experiments to verify its performance. The results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT strategy achieves similar reconstruction and spectral unmixing accuracy as non-spiral dense sampling. Given its ability to dramatically reduce the time required for three-dimensional multispectral scanning, our U3S-PAT strategy has the potential to perform volumetric molecular imaging of dynamic biological activities.
Net 835-Gb/s/λ Carrier- and LO-Free 100-km Transmission Using Channel-Aware Phase Retrieval Reception
Authors: Hanzi Huang, Haoshuo Chen, Qian Hu, Di Che, Yetian Huang, Brian Stern, Nicolas K. Fontaine, Mikael Mazur, Lauren Dallachiesa, Roland Ryf, Zhengxuan Li, Yingxiong Song
Date: 2024-04-10 00:00:00
Tasks: Retrieval

We experimentally demonstrate the first carrier- and LO-free 800G/{\lambda} receiver enabling direct compatibility with standard coherent transmitters via phase retrieval, achieving net 835-Gb/s transmission over 100-km SMF and record 8.27-b/s/Hz net optical spectral efficiency.
Encoder-Quantization-Motion-based Video Quality Metrics
Authors: Yixu Chen, Zaixi Shang, Hai Wei, Yongjun Wu, Sriram Sethuraman
Date: 2024-04-09 00:00:00
Tasks: Quantization, Video Compression

In an adaptive bitrate streaming application, the efficiency of video compression and the encoded video quality depend on both the video codec and the quality metric used to perform encoding optimization. The development of such a quality metric need large scale subjective datasets. In this work we merge several datasets into one to support the creation of a metric tailored for video compression and scaling. We proposed a set of HEVC lightweight features to boost performance of the metrics. Our metrics can be computed from tightly coupled encoding process with 4% compute overhead or from the decoding process in real-time. The proposed method can achieve better correlation than VMAF and P.1204.3. It can extrapolate to different dynamic ranges, and is suitable for real-time video quality metrics delivery in the bitstream. The performance is verified by in-distribution and cross-dataset tests. This work paves the way for adaptive client-side heuristics, real-time segment optimization, dynamic bitrate capping, and quality-dependent post-processing neural network switching, etc.
Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends
Authors: Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke
Date: 2024-04-09 00:00:00
Tasks: Data Augmentation, Deep Learning, Earth Observation

Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive review of the latest developments in this field, focusing on methodologies, challenges, and emerging trends. Deep learning architectures such as Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs) are examined for their suitability in processing hyperspectral data. Key challenges, including limited training data and computational constraints, are identified, along with strategies such as data augmentation and noise reduction using GANs. The paper discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly Field Programmable Gate Arrays (FPGAs), for enhancing processing efficiency is explored. The review concludes with insights into ongoing research trends, including the integration of deep learning techniques into Earth observation missions such as the CHIME mission, and emphasizes the need for further exploration and refinement of deep learning methodologies to address the evolving demands of hyperspectral image processing.
Secrecy Enhancement for UAV-enabled Integrated Sensing and Communication System
Authors: Chaedam Son, Seongah Jeong
Date: 2024-04-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this correspondence, we propose an unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where a full-duplex UAV equipped with uniform planar array (UPA) is adopted as a base station for the multiuser downlink communications, while sensing and jamming a passive ground eavesdropper. The goal of this work is to maximize the sum secrecy rate of ground users subject to the constraints of sensing accuracy and UAV's operational capability by jointly optimizing the transceiver beamforming and UAV's trajectory. To this end, we develop the algorithmic solution based on block coordinate descent (BCD) and semidefinite programming (SDP) relaxation techniques, whose performance is verified via simulations indicating its efficacy in improving communication security with the sufficient mission period.
A 4x32Gb/s 1.8pJ/bit Collaborative Baud-Rate CDR with Background Eye-Climbing Algorithm and Low-Power Global Clock Distribution
Authors: Jihee Kim, Jia Park, Jiwon Shin, Hanseok Kim, Kahyun Kim, Haengbeom Shin, Ha-Jung Park, Woo-Seok Choi
Date: 2024-04-10 00:00:00

This paper presents design techniques for an energy-efficient multi-lane receiver (RX) with baud-rate clock and data recovery (CDR), which is essential for high-throughput low-latency communication in high-performance computing systems. The proposed low-power global clock distribution not only significantly reduces power consumption across multi-lane RXs but is capable of compensating for the frequency offset without any phase interpolators. To this end, a fractional divider controlled by CDR is placed close to the global phase locked loop. Moreover, in order to address the sub-optimal lock point of conventional baud-rate phase detectors, the proposed CDR employs a background eye-climbing algorithm, which optimizes the sampling phase and maximizes the vertical eye margin (VEM). Fabricated in a 28nm CMOS process, the proposed 4x32Gb/s RX shows a low integrated fractional spur of -40.4dBc at a 2500ppm frequency offset. Furthermore, it improves bit-error-rate performance by increasing the VEM by 17%. The entire RX achieves the energy efficiency of 1.8pJ/bit with the aggregate data rate of 128Gb/s.