pwc-archive/papers-with-abstracts huggingface dataset:
T-VSL: Text-Guided Visual Sound Source Localization in Mixtures
Authors: Tanvir Mahmud, Yapeng Tian, Diana Marculescu
Date: 2024-04-02 00:00:00
Proceeding: CVPR 2024 1
Tasks: Sound Source Localization

Visual sound source localization poses a significant challenge in identifying the semantic region of each sounding source within a video. Existing self-supervised and weakly supervised source localization methods struggle to accurately distinguish the semantic regions of each sounding object, particularly in multi-source mixtures. These methods often rely on audio-visual correspondence as guidance, which can lead to substantial performance drops in complex multi-source localization scenarios. The lack of access to individual source sounds in multi-source mixtures during training exacerbates the difficulty of learning effective audio-visual correspondence for localization. To address this limitation, in this paper, we propose incorporating the text modality as an intermediate feature guide using tri-modal joint embedding models (e.g., AudioCLIP) to disentangle the semantic audio-visual source correspondence in multi-source mixtures. Our framework, dubbed T-VSL, begins by predicting the class of sounding entities in mixtures. Subsequently, the textual representation of each sounding source is employed as guidance to disentangle fine-grained audio-visual source correspondence from multi-source mixtures, leveraging the tri-modal AudioCLIP embedding. This approach enables our framework to handle a flexible number of sources and exhibits promising zero-shot transferability to unseen classes during test time. Extensive experiments conducted on the MUSIC, VGGSound, and VGGSound-Instruments datasets demonstrate significant performance improvements over state-of-the-art methods. Code is released at https://github.com/enyac-group/T-VSL/tree/main
Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network
Authors: Md Hassanuzzaman, Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I Ahmed, Ahsan H Khandoker, Raqibul Mostafa
Date: 2024-03-30 00:00:00
Tasks: Sound Classification

Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound.
Data-Driven Room Acoustic Modeling Via Differentiable Feedback Delay Networks With Learnable Delay Lines
Authors: Alessandro Ilic Mezza, Riccardo Giampiccolo, Enzo De Sena, Alberto Bernardini
Date: 2024-03-29 00:00:00

Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a Feedback Delay Network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually-motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics, and outperforms existing methods based on genetic algorithms and analytical FDN design.
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
Authors: Xihao Xie, Jia Zhang, Rahul Ramachandran, Tsengdar J. Lee, Seungwon Lee
Date: 2024-03-30 00:00:00
Tasks: Decision Making, Sentence

Increasingly, more software services have been published onto the Internet, making it a big challenge to recommend services in the process of a scientific workflow composition. In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance. Inspired by natural language sentence generation, the composition process of a scientific workflow is formalized as a step-wise procedure within the context of the goal of workflow, and the problem of next service recommendation is mapped to next word prediction. Historical service dependencies are first extracted from scientific workflow provenance to build a knowledge graph. Service sequences are then generated based on diverse composition path generation strategies. Afterwards, the generated corpus of composition paths are leveraged to study previous decision making strategies. Such a trained goal-oriented next service prediction model will be used to recommend top K candidate services during workflow composition process. Extensive experiments on a real-word repository have demonstrated the effectiveness of this approach.
Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications
Authors: Maico H. W. Engelaar, Zengjie Zhang, Eleftherios E. Vlahakis, Dimos V. Dimarogonas, Mircea Lazar, Sofie Haesaert
Date: 2024-04-02 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Authors: Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis, Andrea Iannelli, John Lygeros
Date: 2024-04-02 00:00:00

Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty. However, such methods are often impossible to implement directly, as they depend on an unobserved estimation error. We consider the equivalent discrete-time setting with a causal information structure, and propose a novel, online proximal point method-based adaptive controller, that under a sufficient excitation (SE) condition is asymptotically stable and achieves finite regret, scaling only with the time required to fulfill the SE. We show the same also for the widely-used recursive least squares with exponential forgetting controller under a stronger persistence of excitation condition.
On the Effect of Quantization on Dynamic Mode Decomposition
Authors: Dipankar Maity, Debdipta Goswami, Sriram Narayanan
Date: 2024-04-02 00:00:00
Tasks: Quantization

Dynamic Mode Decomposition (DMD) is a widely used data-driven algorithm for estimating the Koopman Operator.This paper investigates how the estimation process is affected when the data is quantized. Specifically, we examine the fundamental connection between estimates of the operator obtained from unquantized data and those from quantized data. Furthermore, using the law of large numbers, we demonstrate that, under a large data regime, the quantized estimate can be considered a regularized version of the unquantized estimate. This key theoretical finding paves the way to accurately recover the unquantized estimate from quantized data. We also explore the relationship between the two estimates in the finite data regime. The theory is validated through repeated numerical experiments conducted on three different dynamical systems.
Heuristic Optimization of Amplifier Reconfiguration Process for Autonomous Driving Optical Networks
Authors: Qizhi Qiu, Xiaomin Liu, Yihao Zhang, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2024-04-02 00:00:00
Tasks: Autonomous Driving

We propose a heuristic-based optimization scheme for reliable optical amplifier reconfiguration process in ADON. In the experiment on a commercial testbed, the scheme prevents a 1.0-dB Q-factor degradation and outperforms 98.5% random solutions.
Learning-based model augmentation with LFRs
Authors: Jan H. Hoekstra, Chris Verhoek, Roland Tóth, Maarten Schoukens
Date: 2024-04-02 00:00:00
Tasks: Computational Efficiency, model

Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. In particular, recent encoder-based methods for artificial neural networks state-space (ANN-SS) models have achieved state-of-the-art performance on various benchmarks, while offering consistency and computational efficiency. Inclusion of prior knowledge of the system can be exploited to increase (i) estimation speed, (ii) accuracy, and (iii) interpretability of the resulting models. This paper proposes an encoder-based model augmentation method that incorporates prior knowledge from first-principles (FP) models. We introduce a novel \linear-fractional-representation (LFR) model structure that allows for the unified representation of various augmentation structures including the ones that are commonly used in the literature, and an identification algorithm for estimating the proposed structure together with appropriate initialization methods. The performance and generalization capabilities of the proposed method are demonstrated in a hardening mass-spring-damper simulation.
On the reduction of Linear Parameter-Varying State-Space models
Authors: E. Javier Olucha, Bogoljub Terzin, Amritam Das, Roland Tóth
Date: 2024-04-02 00:00:00
Tasks: Benchmarking, Dimensionality Reduction, Scheduling, State Space Models

This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR, the following methods are compared: Linear Time-Invariant (LTI), LPV and LFR-based balanced reductions, moment matching and parameter-varying oblique projection. For SDR, the following methods are compared: Principal Component Analysis (PCA), trajectory PCA, Kernel PCA and LTI balanced truncation, autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations, from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model.
Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks
Authors: Ahrar N. Hamad, Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2024-04-02 00:00:00
Tasks: Diversity

The increasing demand for wireless networks of higher capacity requires key-enabling technologies. Optical wireless communication (OWC) arises as a complementary technology to radio frequency (RF) systems that can support high aggregate data rates. However, OWC systems face some challenges including beam-blockage. Intelligent reflecting surfaces (IRSs) can offer alternative pathways for the optical signal, ensuring continuous connectivity. In this work, we investigate the potential of using IRS in an indoor OWC network. In particular, we define a system model of indoor OWC that employs IRS in conjunction with angle diversity transmitters (ADT) using vertical-cavity surface-emitting laser (VCSEL) arrays. The VCSEL beam is narrow, directed, and easy to block, however, it can deliver high data rates under eye safety regulations. Simulation results show that the deployment of IRS can significantly improve the achievable data rates of Laser-based OWC systems.
Model Predictive Control of District Heating Grids Using Stabilizing Terminal Ingredients
Authors: Max Rose, Hannes Gernandt, Juan E. Machado, Johannes Schiffer
Date: 2024-04-02 00:00:00
Tasks: Model Predictive Control

The transformation of fossil fuel-based district heating grids (DHGs) to CO$_2$-neutral DHGs requires the development of novel operating strategies. Model predictive control (MPC) is a promising approach, as knowledge about future heat demand and heat supply can be incorporated into the control, operating constraints can be ensured and the stability of the closed-loop system can be guaranteed. In this paper, we employ MPC for DHGs to control the system mass flows and injected heat flows. Following common practice, we derive terminal ingredients to stabilize given steady state temperatures and storage masses in the DHG. To apply MPC with terminal ingredients, it is crucial that the system under control is stabilizable. By exploiting the particular system structure, we give a sufficient condition for the stabilizability in terms of the grid topology and hence, for the applicability of the MPC scheme to DHGs. Furthermore, we demonstrate the practicability of the application of MPC to an exemplary DHG in a numerical case study.
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
Authors: Thom Badings, Licio Romao, Alessandro Abate, Nils Jansen
Date: 2024-04-02 00:00:00

Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction methods often lead to prohibitively large finite-state models. To address this issue, we propose a novel abstraction scheme for stochastic linear systems that exploits the system's stability to obtain significantly smaller abstract models. As a unique feature, we first stabilize the open-loop dynamics using a linear feedback gain. We then use a model-based approach to abstract a known part of the stabilized dynamics while using a data-driven method to account for the stochastic uncertainty. We formalize abstractions as Markov decision processes (MDPs) with intervals of transition probabilities. By stabilizing the dynamics, we can further constrain the control input modeled in the abstraction, which leads to smaller abstract models while retaining the correctness of controllers. Moreover, when the stabilizing feedback controller is aligned with the property of interest, then a good trade-off is achieved between the reduction in the abstraction size and the performance loss. The experiments show that our approach can reduce the size of the graph of abstractions by up to 90% with negligible performance loss.
On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems
Authors: Mauro Salazar, Sara Betancur Giraldo, Fabio Paparella, Leonardo Pedroso
Date: 2024-03-30 00:00:00
Tasks: Fairness

Research on the operation of mobility systems so far has mostly focused on minimizing cost-centered metrics such as average travel time, distance driven, and operational costs. Whilst capturing economic indicators, such metrics do not account for transportation justice aspects. In this paper, we present an optimization model to plan the operation of Intermodal Autonomous Mobility-on-Demand (I-AMoD) systems, where self-driving vehicles provide on-demand mobility jointly with public transit and active modes, with the goal to minimize the accessibility unfairness experienced by the population. Specifically, we first leverage a previously developed network flow model to compute the I-AMoD system operation in a minimum-time manner. Second, we formally define accessibility unfairness, and use it to frame the minimum-accessibility-unfairness problem and cast it as a linear program. We showcase our framework for a real-world case-study in the city of Eindhoven, NL. Our results show that it is possible to reach an operation that is on average fully fair at the cost of a slight travel time increase compared to a minimum-travel-time solution. Thereby we observe that the accessibility fairness of individual paths is, on average, worse than the average values obtained from flows, setting the stage for a discussion on the definition of accessibility fairness itself.
Identifying the Largest RoCoF and Its Implications
Authors: Licheng Wang, Luochen Xie, Gang Huang, Changsen Feng
Date: 2024-04-02 00:00:00

The rate of change of frequency (RoCoF) is a critical factor in ensuring frequency security, particularly in power systems with low inertia. Currently, most RoCoF security constrained optimal inertia dispatch methods and inertia market mechanisms predominantly rely on the center of inertia (COI) model. This model, however, does not account for the disparities in post-contingency frequency dynamics across different regions of a power system. Specifically, regional buses can exhibit significantly larger RoCoFs than that predicted by the system's COI, particularly in systems characterized by unevenly distributed inertia. In this letter, a post-contingency nodal RoCoF model is established, and the maximal initial RoCoF is further proven to occur at generator buses equipped with inertia, rather than at inertia-less load buses. This finding facilitates the development of the optimal nodal inertia dispatch method and the nodal inertia market mechanism in a convex and concise form. Our argument is further verified by the simulation results of the South East Australia power system under various scenarios.
Real-Time Hybrid Simulation for Infrastructure Degradation Assessment: Conceptual Framework and Illustrative Application
Authors: Manuel Salmeron, Herta Montoya, Edwin Patino, Ingrid E. Madera Sierra, Shirley J. Dyke
Date: 2024-04-02 00:00:00

To date, the prospect of using real-time hybrid simulation (RTHS) to study the effects of long-term or 'wear-and-tear' loads, such as exposure to harmful environmental conditions or fatigue, has remained underexplored. This study presents a conceptual framework to assess the impact of long-term degradation on infrastructure systems. The framework integrates the capabilities of RTHS with accelerated degradation techniques to evaluate the behavior of a degrading system over time. Experimental results obtained in this way not only capture the complex interactions but also provide a reliability-based method to determine the expected time-to-failure of the evaluated system. The developed framework is demonstrated using a virtual RTHS platform designed to test fiber-reinforced elastomeric isolators.
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
Authors: Sarupa Debnath, Bernard T. Agyeman, Soumya R. Sahoo, Xunyuan Yin, Jinfeng Liu
Date: 2024-04-01 00:00:00
Tasks: Prediction, Soil moisture estimation

Accurate soil moisture information is crucial for developing precise irrigation control strategies to enhance water use efficiency. Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields. The major challenge in soil moisture estimation lies in the high dimensionality of the spatially discretized agro-hydrological models. In this work, we propose a performance-triggered adaptive model reduction approach to address this challenge. The pro- posed approach employs a trajectory-based unsupervised machine learning technique, and a prediction performance-based triggering scheme is designed to govern model updates adaptively in a way such that the prediction error between the reduced model and the original model over a prediction horizon is maintained below a predetermined threshold. An adaptive extended Kalman filter (EKF) is designed based on the reduced model for soil moisture estimation. The applicability and performance of the proposed approach are evaluated extensively through the application to a simulated large-scale agricultural field.
Using Dynamic Safety Margins as Control Barrier Functions
Authors: Victor Freire, Marco M. Nicotra
Date: 2024-04-01 00:00:00

This paper provides a systematic approach to design control barrier functions (CBFs) for arbitrary state and input constraints using tools from the reference governor literature. In particular, it is shown that dynamic safety margins (DSMs) are CBFs for an augmented system obtained by concatenating the state and a virtual reference. The proposed approach is agnostic to the relative degree and can handle multiple state and input constraints using the control-sharing property of CBFs. The construction of CBFs using Lyapunov-based DSMs is then investigated in further detail. Numerical simulations show that the method outperforms existing DSM-based approaches, while also guaranteeing safety and persistent feasibility of the associated optimization program.
Unified Predefined-time Stability Conditions of Nonlinear Systems with Lyapunov Analysis
Authors: Bing Xiao, Haichao Zhang, Shijie Zhao, Lu Cao
Date: 2024-04-01 00:00:00

This brief gives a set of unified Lyapunov stability conditions to guarantee the predefined-time/finite-time stability of a dynamical systems. The derived Lyapunov theorem for autonomous systems establishes equivalence with existing theorems on predefined-time/finite-time stability. The findings proposed herein develop a nonsingular sliding mode control framework for an Euler-Lagrange system to analyze its stability, and its upper bound for the settling time can be arbitrarily determined a priori through predefined time constant.