pwc-archive/papers-with-abstracts huggingface dataset:
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning
Authors: Zixiang Wan, Ziyue Qiu, Yiyang Liu, Wei-Qiang Zhang
Date: 2024-12-30 00:00:00
Tasks: Emotion Recognition, Multi-Task Learning, Self-Supervised Learning, Speech Emotion Recognition

Speech Emotion Recognition (SER) involves analyzing vocal expressions to determine the emotional state of speakers, where the comprehensive and thorough utilization of audio information is paramount. Therefore, we propose a novel approach on self-supervised learning (SSL) models that employs all available auxiliary information -- specifically metadata -- to enhance performance. Through a two-stage fine-tuning method in multi-task learning, we introduce the Augmented Residual Integration (ARI) module, which enhances transformer layers in encoder of SSL models. The module efficiently preserves acoustic features across all different levels, thereby significantly improving the performance of metadata-related auxiliary tasks that require various levels of features. Moreover, the Co-attention module is incorporated due to its complementary nature with ARI, enabling the model to effectively utilize multidimensional information and contextual relationships from metadata-related auxiliary tasks. Under pre-trained base models and speaker-independent setup, our approach consistently surpasses state-of-the-art (SOTA) models on multiple SSL encoders for the IEMOCAP dataset.
Bird Vocalization Embedding Extraction Using Self-Supervised Disentangled Representation Learning
Authors: Runwu Shi, Katsutoshi Itoyama, Kazuhiro Nakadai
Date: 2024-12-28 00:00:00
Tasks: Representation Learning

This paper addresses the extraction of the bird vocalization embedding from the whole song level using disentangled representation learning (DRL). Bird vocalization embeddings are necessary for large-scale bioacoustic tasks, and self-supervised methods such as Variational Autoencoder (VAE) have shown their performance in extracting such low-dimensional embeddings from vocalization segments on the note or syllable level. To extend the processing level to the entire song instead of cutting into segments, this paper regards each vocalization as the generalized and discriminative part and uses two encoders to learn these two parts. The proposed method is evaluated on the Great Tits dataset according to the clustering performance, and the results outperform the compared pre-trained models and vanilla VAE. Finally, this paper analyzes the informative part of the embedding, further compresses its dimension, and explains the disentangled performance of bird vocalizations.
Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems
Authors: Saiedeh Akbari, Cristian F. Nino, Omkar Sudhir Patil, Warren E. Dixon
Date: 2024-12-30 00:00:00

Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such complex systems, deep neural networks can serve as powerful black box approximators for the unknown drift and diffusion processes. Recent developments construct Lyapunov-based deep neural network (Lb-DNN) controllers to compensate for deterministic uncertainties using adaptive weight update laws derived from a Lyapunov-based analysis based on insights from the compositional structure of the DNN architecture. However, these Lb-DNN controllers do not account for non-deterministic uncertainties. This paper develops Lb-DNNs to adaptively compensate for both the drift and diffusion uncertainties of nonlinear stochastic dynamic systems. Through a Lyapunov-based stability analysis, a DNN-based approximation and corresponding DNN weight adaptation laws are constructed to eliminate the unknown state-dependent terms resulting from the nonlinear diffusion and drift processes. The tracking error is shown to be uniformly ultimately bounded in probability. Simulations are performed on a nonlinear stochastic dynamical system to show efficacy of the proposed method.
Practical Implementation and Experimental Validation of an Optimal Control based Eco-Driving System
Authors: Vinith Kumar Lakshmanan, Olivier Lemaire, Antonio Sciarretta
Date: 2024-12-30 00:00:00

The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates the energy gains of an ED system for an electric vehicle, obtained from a predictive optimal controller, in a real-world traffic scenario. To this end, a Visual driver Advisory System (VAS) in the form of a personal tablet is used to advise the driver to follow a target eco-speed via a screen. Two Renault Zoe electric cars, one equipped with the different modules for ED and one without, are used to perform field tests on a route between Rueil-Malmaison and Bougival in France. Overall, the ED consumed, on average, 4.6~$\%$ less energy than the non-eco-driven car with a maximum of 2~$\%$ change in average speed.
Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients
Authors: Dongdong Li, Jiuxiang Dong
Date: 2024-12-30 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy $\mathcal{Q}$-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation.
An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals
Authors: Yifei Sun, Chao Yu, Yan Luo, Tony Xiao Han, Haisheng Tan, Rui Wang, Francis C. M. Lau
Date: 2024-12-30 00:00:00

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a multi-target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy.
Ultra-Wideband Double-Directional Channel Measurements and Statistical Modeling in Urban Microcellular Environments for the Upper-Midband/FR3
Authors: Naveed A. Abbasi, Kelvin Arana, Siddhant Singh, Atulya Bist, Vikram Vasudevan, Tathagat Pal, Jorge Gomez-Ponce, Young-Han Nam, Charlie Zhang, Andreas F. Molisch
Date: 2024-12-30 00:00:00

The upper midband, designated as Frequency Range 3 (FR3), is increasingly critical for the next-generation of wireless networks. Channel propagation measurements and their statistical analysis are essential first steps towards this direction. This paper presents a comprehensive ultra-wideband (UWB) double-directional channel measurement campaign in a large portion of FR3 (6-14 GHz) for urban microcellular environments. We analyze over 25,000 directional power delay profiles and providing key insights into line-of-sight (LoS) and obstructed line-of-sight (OLoS) conditions. This is followed by statistical modeling of path loss, shadowing, delay spread and angular spread. As the first UWB double-directional measurement campaign in this frequency range, this work offers critical insights for spectrum allocation, channel modeling, and the design of advanced communication systems, paving the way for further exploration of FR3.
Highway Managed Lane Usage and Tolling for Mixed Traffic Flows with Connected Automated Vehicles (CAVs) and High-Occupancy Vehicles (HOVs)
Authors: Max T. M. Ng, Hani S. Mahmassani
Date: 2024-12-30 00:00:00

This paper investigates managed lane (ML) toll setting and its effect under mixed traffic of connected automated vehicles (CAVs), high-occupancy vehicles (HOVs), and human-driven vehicles (HDVs), with a goal to avoid flow breakdown and minimize total social cost. A mesoscopic finite-difference traffic simulation model considers the flow-density relationship at different CAV market penetration rates, lane-changing behavior, and multiple entries/exits, interacting with a reactive toll setting mechanism. The results of the Monte Carlo simulation suggest an optimal policy of untolled HOV/CAV use with HDV tolls in particular scenarios of limited CAV market penetration. Small and targeted tolling avoids flow breakdown in ML while prioritizing HOVs and other vehicles with high values of time. Extensions of the formulation and sensitivity analysis quantify the benefits of converting high-occupancy HDVs to CAVs. The optimal tolling regime combines traffic science notions of flow stability and the economics of resource allocation.
Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem
Authors: Michael fogel, Snigdha Sushil Mishra, Laurent Burlion
Date: 2024-12-30 00:00:00

Sloshing Platform for In-Orbit Controller Experimentation is an ambitious, student run mission to design and fly a cubesat to study fluid sloshing in spacecraft. The project will examine zero-g propellant sloshing from an experimental standpoint. Despite the small size and limited payload capacity, we intend to use the cubesat platform to mimic larger spacecraft and implement novel detection and computer vision methods in our analysis. Many modern spacecraft rely on propellant-filled tanks to perform attitude control and station-keeping maneuvers. When a large percentage of the spacecraft's mass is comprised of liquid propellant, sloshing becomes a critical aspect of spacecraft attitude control and stability. The mission will study the tank/fluid dynamics using new methods to gain an enhanced understanding of low-gravity fluid disturbance effects and improve simulations using equivalent mechanical models (EMMs). Active control of the fluid leading to the reduction of propellant slosh settling times will improve the maneuverability and performance of spacecraft. This paper will focus on satellite payload research and design requirements used to inform other aspects of the SPICEsat design. In this paper, mission objectives will be discussed, numerical simulations for the proposed control algorithms are demonstrated, and a satellite experiment design is presented. Finally, we examine computational fluid dynamics models to validate the satellite design and propellant sensing components of the proposed spacecraft.
Low-Thrust Under-Actuated Satellite Formation Guidance and Control Strategies
Authors: Ahmed Mahfouz, Gabriella Gaias, Florio Dalla Vedova, Holger Voos
Date: 2024-12-29 00:00:00
Tasks: Earth Observation, Model Predictive Control

This study presents autonomous guidance and control strategies for the purpose of reconfiguring close-range multi-satellite formations. The formation under consideration includes $N$ under-actuated deputy satellites and an uncontrolled virtual or physical chief spacecraft. The guidance problem is formulated as a trajectory optimization problem that incorporates typical dynamical and physical constraints, alongside a minimum acceleration threshold. This latter constraint arises from the physical limitations of the adopted low-thrust technology, which is commonly employed for precise, close-range relative orbital maneuvers. The guidance and control problem is addressed in two frameworks: centralized and distributed. The centralized approach provides a fuel-optimal solution, but it is practical only for formations with a small number of deputies. The distributed approach is more scalable but yields sub-optimal solutions. In the centralized framework, the chief is a physical satellite responsible for all calculations, while in the distributed framework, the chief is treated as a virtual point mass orbiting the Earth, and each deputy performs its own guidance and control calculations onboard. The study emphasizes the spaceborne implementation of the closed-loop control system, aiming for a reliable and automated solution to the optimal control problem. To this end, the risk of infeasibility is mitigated through first identifying the constraints that pose a potential threat of infeasibility, then properly softening them. Two Model Predictive Control architectures are implemented and compared, namely, a shrinking-horizon and a fixed-horizon schemes. Performances, in terms of fuel expenditure and achieved control accuracy, are analyzed on typical close-range reconfigurations requested by Earth observation missions and are compared against different implementations proposed in the literature.
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks
Authors: Seyyed Shaho Alaviani, Atul Kelkar
Date: 2024-12-29 00:00:00
Tasks: Distributed Optimization

This paper aims at distributed multi-agent convex optimization where the communications network among the agents are presented by a random sequence of possibly state-dependent weighted graphs. This is the first work to consider both random arbitrary communication networks and state-dependent interactions among agents. The state-dependent weighted random operator of the graph is shown to be quasi-nonexpansive; this property neglects a priori distribution assumption of random communication topologies to be imposed on the operator. Therefore, it contains more general class of random networks with or without asynchronous protocols. A more general mathematical optimization problem than that addressed in the literature is presented, namely minimization of a convex function over the fixed-value point set of a quasi-nonexpansive random operator. A discrete-time algorithm is provided that is able to converge both almost surely and in mean square to the global solution of the optimization problem. Hence, as a special case, it reduces to a totally asynchronous algorithm for the distributed optimization problem. The algorithm is able to converge even if the weighted matrix of the graph is periodic and irreducible under synchronous protocol. Finally, a case study on a network of robots in an automated warehouse is given where there is distribution dependency among random communication graphs.
High-Performance Model Predictive Control for Quadcopters with Formal Stability Guarantees
Authors: Maedeh Izadi, A. T. J. R. Cobbenhagen, Ruben Sommer, A. R. P. Andriën, Erjen Lefeber, W. P. M. H. Heemels
Date: 2024-12-28 00:00:00
Tasks: Model Predictive Control

In this paper, we present a novel cascade control structure with formal guarantees of uniform almost global asymptotic stability for the state tracking error dynamics of a quadcopter. The proposed approach features a model predictive control strategy for the outer loop, explicitly accounting for the non-zero total thrust constraint. The outer-loop controller generates an acceleration reference, which is then converted into attitude, angular velocity and acceleration references, subsequently tracked by a nonlinear inner-loop controller. The proposed cascade control strategy is validated through numerical case studies, underlying high-fidelity models, demonstrating its ability to track fast trajectories with small error.
Towards influence centrality: where to not add an edge in the network?
Authors: Aashi Shrinate, Twinkle Tripathy
Date: 2024-12-28 00:00:00
Tasks: Decision Making

In this work, we consider a strongly connected group of individuals involved in decision-making. The opinions of the individuals evolve using the Friedkin-Johnsen (FJ) model. We consider that there are two competing `influencers' (stubborn agents) vying for control over the final opinion of the group. We investigate the impact of modifying the network interactions on their respective control over the final opinions (influence centrality). We use signal flow graphs (SFG) to relate the network interactions with the influence that each `influencer' exerts on others. We present the sufficient conditions on the edge modifications which lead to the increase of the influence of an `influencer' at the expense of the other. Interestingly, the analysis also reveals the existence of redundant edge modifications that result in no change in the influence centrality of the network. We present several numerical examples to illustrate these results.
Online Low-Carbon Workload, Energy, and Temperature Management of Distributed Data Centers
Authors: Rui Xie, Yue Chen, Xi Weng
Date: 2024-12-28 00:00:00
Tasks: Management

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the workload, energy, and temperature strategies across distributed data centers, targeting minimal total cost, controlled carbon emissions, and adherence to operational constraints. Lyapunov optimization technique is adopted to derive a parametric real-time strategy that accommodates uncertainties in workload demands, ambient temperature, electricity prices, and carbon intensities, without requiring prior knowledge of their distributions. A theoretical upper bound for the optimality gap is derived, based on which a linear programming problem is proposed to optimize the strategy parameters, enhancing performance while ensuring operational constraints. Case studies and method comparison validate the proposed method's effectiveness in reducing costs and carbon emissions.
The FlEye camera: Sampling the joint distribution of natural scenes and motion
Authors: Charles J. Edelson, Paul Smith, Sima Setayeshgar, William Bialek, Rob R. de Ruyter van Steveninck
Date: 2024-12-30 00:00:00
Tasks: Motion Estimation

To make efficient use of limited physical resources, the brain must match its coding and computational strategies to the statistical structure of input signals. An attractive testing ground for these principles is the problem of motion estimation in the fly visual system: we understand the optics of the compound eye, have a quantitative description of input signals and noise from the retina, and can record from output neurons that encode estimates of different velocity components. Furthermore, recent work provides a nearly complete wiring diagram of the intervening circuitry. What is missing is a characterization of the visual signals and motions that flies encounter in a natural context. We attack this directly with the development of a specialized camera that matches the high temporal resolution, optical properties, and spectral sensitivity of the fly's eye; inertial motion sensors provide ground truth about rotations and translations through the world. We describe the design, construction, and performance characteristics of this FlEye camera. To illustrate the opportunities created by this instrument we use data on movies and motion to construct optimal local motion estimators that can be compared with the responses of the fly's motion sensitive neurons.
Econometric Analysis of Pandemic Disruption and Recovery Trajectory in the U.S. Rail Freight Industry
Authors: Max T. M. Ng, Hani S. Mahmassani, Joseph L. Schofer
Date: 2024-12-30 00:00:00
Tasks: counterfactual, Time Series Analysis

To measure the impacts on U.S. rail and intermodal freight by economic disruptions of the 2007-09 Great Recession and the COVID-19 pandemic, this paper uses time series analysis with the AutoRegressive Integrated Moving Average (ARIMA) family of models and covariates to model intermodal and commodity-specific rail freight volumes based on pre-disruption data. A framework to construct scenarios and select parameters and variables is demonstrated. By comparing actual freight volumes during the disruptions against three counterfactual scenarios, Trend Continuation, Covariate-adapted Trend Continuation, and Full Covariate-adapted Prediction, the characteristics and differences in magnitude and timing between the two disruptions and their effects across nine freight components are examined. Results show the disruption impacts differ from measurement by simple comparison with pre-disruption levels or year-on-year comparison depending on the structural trend and seasonal pattern. Recovery Pace Plots are introduced to support comparison in recovery speeds across freight components. Accounting for economic variables helps improve model fitness. It also enables evaluation of the change in association between freight volumes and covariates, where intermodal freight was found to respond more slowly during the pandemic, potentially due to supply constraint.
Automated Demand Forecasting in small to medium-sized enterprises
Authors: Thomas Gaertner, Christoph Lippert, Stefan Konigorski
Date: 2024-12-29 00:00:00
Tasks: Additive models, Demand Forecasting

In response to the growing demand for accurate demand forecasts, this research proposes a generalized automated sales forecasting pipeline tailored for small- to medium-sized enterprises (SMEs). Unlike large corporations with dedicated data scientists for sales forecasting, SMEs often lack such resources. To address this, we developed a comprehensive forecasting pipeline that automates time series sales forecasting, encompassing data preparation, model training, and selection based on validation results. The development included two main components: model preselection and the forecasting pipeline. In the first phase, state-of-the-art methods were evaluated on a showcase dataset, leading to the selection of ARIMA, SARIMAX, Holt-Winters Exponential Smoothing, Regression Tree, Dilated Convolutional Neural Networks, and Generalized Additive Models. An ensemble prediction of these models was also included. Long-Short-Term Memory (LSTM) networks were excluded due to suboptimal prediction accuracy, and Facebook Prophet was omitted for compatibility reasons. In the second phase, the proposed forecasting pipeline was tested with SMEs in the food and electric industries, revealing variable model performance across different companies. While one project-based company derived no benefit, others achieved superior forecasts compared to naive estimators. Our findings suggest that no single model is universally superior. Instead, a diverse set of models, when integrated within an automated validation framework, can significantly enhance forecasting accuracy for SMEs. These results emphasize the importance of model diversity and automated validation in addressing the unique needs of each business. This research contributes to the field by providing SMEs access to state-of-the-art sales forecasting tools, enabling data-driven decision-making and improving operational efficiency.