pwc-archive/papers-with-abstracts huggingface dataset:
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
Authors: Vikramjit Mitra, Amrit Romana, Dung T. Tran, Erdrin Azemi
Date: 2025-03-24 00:00:00
Tasks: Emotion Recognition, Overall - Test

Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertainty in labels due to grader opinion variation. Grader variation is addressed by using consensus grades as groundtruth, where the emotion with the highest vote is selected. Consensus grades fail to consider ambiguous instances where a speech sample may contain multiple emotions, as captured through grader opinion uncertainty. We demonstrate that using the probability density function of the emotion grades as targets instead of the commonly used consensus grades, provide better performance on benchmark evaluation sets compared to results reported in the literature. We show that a saliency driven foundation model (FM) representation selection helps to train a state-of-the-art speech emotion model for both dimensional and categorical emotion recognition. Comparing representations obtained from different FMs, we observed that focusing on overall test-set performance can be deceiving, as it fails to reveal the models generalization capacity across speakers and gender. We demonstrate that performance evaluation across multiple test-sets and performance analysis across gender and speakers are useful in assessing usefulness of emotion models. Finally, we demonstrate that label uncertainty and data-skew pose a challenge to model evaluation, where instead of using the best hypothesis, it is useful to consider the 2- or 3-best hypotheses.
Audio Compression using Periodic Gabor with Biorthogonal Exchange: Implementation Using the Zak Transform
Authors: Roger Alimi, David J. Tannor
Date: 2025-03-17 00:00:00
Tasks: Audio Compression, CPU

An efficient new approach to signal compression is presented based of a novel variation on the Gabor basis set. Following earlier work by Shimshovitz and Tannor, we convolve the conventional Gabor functions with Dirichlet functions to obtain a Periodic Gabor basis set (PG). The PG basis is exact for continuous functions that are periodic band-limited. Using the orthonormality of the Dirichlet functions, the calculation of the PG coefficients becomes trivial and numerically stable, but its representation does not allow compression. Large compression factors are achieved by exchanging the PG basis with its biorthogonal basis, thereby using the localized PG basis to calculate the coefficients (PGB). Here we implement the PGB formalism using the Fast Zak Transform and obtain very high efficiency with respect to both CPU and memory. We compare the method with the state of the art Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT) methods on a variety of audio files, including music and speech samples. In all cases tested our scheme surpasses the STFT by far and in most cases outperforms DWT.
Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free Communication
Authors: Guanjie Huang, Danny Hin Kwok Tsang, Li Liu
Date: 2025-03-11 00:00:00
Tasks: Lip Reading, Prompt Engineering, speech-recognition, Speech Recognition, Zero-Shot Learning

Cued Speech (CS) is an innovative visual communication system that integrates lip-reading with hand coding, designed to enhance effective communication for individuals with hearing impairments. Automatic CS Recognition (ACSR) refers to the AI-driven process of automatically recognizing hand gestures and lip movements in CS, converting them into text. However, previous work often relies on complex fusion modules and training techniques. Additionally, due to the limited amount of data in CS, the extraction of hand features, as well as recognition modeling, has consistently been subpar, significantly limiting the effectiveness of ACSR. To address this issue, we have innovatively explored the capabilities of Multimodal large language models (MLLMs) in recognizing hand shapes and positions in CS. More precisely, we propose a new Semi Training-Free paradigm for ACSR, named STF-ACSR. This approach leverages zero-shot recognition of hand movements through the Chinese CS Prompt Module (CCSPM), which equipped a training-free keyframe filtering and customized prompt engineering based on MLLM. It then integrates the recognition results into the lip-reading model using a Minimalist Fusion Module (MFM), effectively achieving superior recognition results. Furthermore, specifically for this study, we have supplemented the existing dataset of 6 normal hearing CS cuers by recording additional data from 8 cuers with hearing impairments, resulting in a new mixed dataset. Extensive experiments have demonstrated that STF-ACSR significantly outperforms previous methods on both normal and hearing-impaired data. Implementation and checkpoints are available at https://github.com/DennisHgj/STF_ACSR.
Model Lake: a New Alternative for Machine Learning Models Management and Governance
Authors: Moncef Garouani, Franck Ravat, Nathalie Valles-Parlangeau
Date: 2025-03-27 00:00:00
Tasks: Management

The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.
Coordinating Distributed Energy Resources with Nodal Pricing in Distribution Networks: a Game-Theoretic Approach
Authors: Eli Brock, Jingqi Li, Javad Lavaei, Somayeh Sojoudi
Date: 2025-03-31 00:00:00

We propose a real-time nodal pricing mechanism for cost minimization and voltage control in a distribution network with autonomous distributed energy resources and analyze the resulting market using stochastic game theory. Unlike existing methods, the proposed pricing scheme does not require device-aware centralized coordination or communication between prosumers. By developing new sufficient conditions under which a stochastic game is a Markov potential game, we show that the problem of computing an equilibrium for the proposed model is equivalent to solving a single-agent Markov Decision Process. These new conditions are general and may apply to other applications. We compute the equilibrium for an IEEE test system to empirically demonstrate the effectiveness of the pricing policy.
Analysis of the French system imbalance paving the way for a novel operating reserve sizing approach
Authors: Jonathan Dumas, Sébastien Finet, Nathalie Grisey, Ibtissam Hamdane, Paul Plessiez
Date: 2025-03-31 00:00:00

This paper examines the relationship between system imbalance and several explanatory variables within the French electricity system. The factors considered include lagged imbalance values, observations of renewable energy sources (RES) generation and consumption, and forecasts for RES generation and consumption. The study analyzes the distribution of system imbalance in relation to these variables. Additionally, an HGBR machine-learning model is employed to assess the predictability of imbalances and the explanatory power of the input variables studied. The results indicate no clear correlation between RES generation or consumption and the observed imbalances. However, it is possible to predict the imbalance adequately using forecasts available a few hours before real-time, along with the lagged values of the imbalance. Predicting the imbalance a day in advance proves to be complex with the variables examined; however, the extreme quantiles of the imbalance used for reserve sizing and contracting can be predicted with sufficient accuracy.
Disturbance-adaptive Model Predictive Control for Bounded Average Constraint Violations
Authors: Jicheng Shi, Colin N. Jones
Date: 2025-03-31 00:00:00
Tasks: Conformal Prediction, Model Predictive Control

This paper considers stochastic linear time-invariant systems subject to constraints on the average number of state-constraint violations over time without knowing the disturbance distribution. We present a novel disturbance-adaptive model predictive control (DAD-MPC) framework, which adjusts the disturbance model based on measured constraint violations. Using a robust invariance method, DAD-MPC ensures recursive feasibility and guarantees asymptotic or robust bounds on average constraint violations. Additionally, the bounds hold even with an inaccurate disturbance model, which allows for data-driven disturbance quantification methods to be used, such as conformal prediction. Simulation results demonstrate that the proposed approach outperforms state-of-the-art methods while satisfying average violation constraints.
Quantifying Grid-Forming Behavior: Bridging Device-level Dynamics and System-Level Stability
Authors: Kehao Zhuang, Huanhai Xin, Verena Häberle, Xiuqiang He, Linbin Huang, Florian Dörfler
Date: 2025-03-31 00:00:00

Grid-Forming (GFM) technology is considered a promising solution to build power electronics-dominated power systems. However, the impact of GFM converters on the system stability is still unquantified, creating a gap between the system- and device-level perspectives. To fill this gap, at the device-level, we propose a Forming Index to quantify a converter's response to grid voltage variations, providing a characterization of its GFM behavior. At the system-level, a quantitative notion of System Strength is introduced to capture the fundamental requirements for power system formation. Finally, we establish the alignment between device- and system-level metrics by demonstrating that GFM converters provably enhance system strength.
Data-Driven Distributed Output Synchronization of Heterogeneous Discrete-Time Multi-Agent Systems
Authors: Giulio Fattore, Maria Elena Valcher
Date: 2025-03-31 00:00:00

In this paper, we assume that an autonomous exosystem generates a reference output, and we consider the problem of designing a distributed data-driven control law for a family of discrete-time heterogeneous LTI agents, connected through a directed graph, in order to synchronize the agents' outputs to the reference one. The agents of the network are split into two categories: leaders, with direct access to the exosystem output, and followers, that only receive information from their neighbors. All agents aim to achieve output synchronization by means of a state feedback that makes use of their own states as well as of an estimate of the exogenous system state, provided by an internal state observer. Such observer has a different structure for leaders and followers. Necessary and sufficient conditions for the existence of a solution are first derived in the model-based set-up and then in a data-driven context. An example illustrates both the implementation procedure and the performance of the proposed approach.
Application of Battery Storage to Switching Predictive Control of Power Distribution Systems Including Road Heating
Authors: Chiaki Kojima, Yuya Muto, Hikaru Akutsu, Rinnosuke Shima, Yoshihiko Susuki
Date: 2025-03-31 00:00:00

In regions with heavy snowfall, the living environment is becoming a serious problem due to heavy snow accumulation. A road heating is an electrical device which promotes snow melting by burying a heating cable as a thermal source underground in such regions. When integrating the road heating into power distribution systems, we need to optimize the flow of electric power by appropriately integrating distributed power sources and conventional power distribution equipment. In this paper, we introduce a battery storage to the power distribution system including road heating, and extend the predictive switching control of the systems due to the authors' previous study to the case where battery storage is installed. As a main result, we propose a predictive switching control that utilizes photovoltaic (PV) power generation and surplus power stored in the battery storage effectively, and achieves the reduction of distribution loss, attenuation of voltage fluctuation, and efficient snow melting, simultaneously. We verify the effectiveness of the application of battery storage through numerical simulation using actual time series data of weather conditions and active power of the PV power generation and load.
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
Authors: Le Liu, Yu Kawano, Antai Xie, Ming Cao
Date: 2025-03-31 00:00:00

In this paper, we investigate initial state privacy protection for discrete-time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances. A new differential privacy condition is formulated using incremental output boundedness, enabling the design of time-varying Laplacian noise to achieve specified privacy guarantees. The proposed framework extends beyond initial state protection to also cover system parameter privacy, which is demonstrated as a special application.
Unraveling tensor structures in correct-by-design controller synthesis
Authors: Ruohan Wang, Zhiyong Sun, Sofie Haesaert
Date: 2025-03-31 00:00:00

Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes. As the state space of these MDPs grows, the curse of dimensionality makes the computational and memory cost of the probabilistic guarantees, quantified with dynamic programming, scale exponentially. In this work, we leverage decoupled dynamics and unravel, via dynamic programming operations, a tree structure in the Canonical Polyadic Decomposition (CPD) of the value functions. For discrete-time stochastic systems with syntactically co-safe linear temporal logic (scLTL) specifications, we provide provable probabilistic safety guarantees and significantly alleviate the computational burden. We provide an initial validation of the theoretical results on several typical case studies and showcase that the uncovered tree structure enables efficient reductions in the computational burden.
A robot-assisted pipeline to rapidly scan 1.7 million historical aerial photographs
Authors: Sheila Masson, Alan Potts, Allan Williams, Steve Berggreen, Kevin McLaren, Sam Martin, Eugenio Noda, Nicklas Nordfors, Nic Ruecroft, Hannah Druckenmiller, Solomon Hsiang, Andreas Madestam, Anna Tompsett
Date: 2025-03-31 00:00:00
Tasks: Retrieval

During the 20th Century, aerial surveys captured hundreds of millions of high-resolution photographs of the earth's surface. These images, the precursors to modern satellite imagery, represent an extraordinary visual record of the environmental and social upheavals of the 20th Century. However, most of these images currently languish in physical archives where retrieval is difficult and costly. Digitization could revolutionize access, but manual scanning is slow and expensive. Here, we describe and validate a novel robot-assisted pipeline that increases worker productivity in scanning 30-fold, applied at scale to digitize an archive of 1.7 million historical aerial photographs from 65 countries.
An ANN-Enhanced Approach for Flatness-Based Constrained Control of Nonlinear Systems
Authors: Huu-Thinh Do, Ionela Prodan, Florin Stoican
Date: 2025-03-31 00:00:00
Tasks: Model Predictive Control

Neural networks have proven practical for a synergistic combination of advanced control techniques. This work analyzes the implementation of rectified linear unit neural networks to achieve constrained control in differentially flat systems. Specifically, the class of flat systems enjoys the benefit of feedback linearizability, i.e., the systems can be linearized by means of a proper variable transformation. However, the price for linearizing the dynamics is that the constraint descriptions are distorted geometrically. Our results show that, by using neural networks, these constraints can be represented as a union of polytopes, enabling the use of mixed-integer programming tools to guarantee constraint satisfaction. We further analyze the integration of the characterization into efficient settings such as control Lyapunov function-based and model predictive control (MPC). Interestingly, this description also allows us to explicitly compute the solution of the MPC problem for the nonlinear system. Several examples are provided to illustrate the effectiveness of our framework.
Two-wheel-driven Electric Superbike Powertrain Optimization
Authors: Adelmo Niccolai, Maurizio Clemente, Theo Hofman, Niccolò Baldanzini
Date: 2025-03-31 00:00:00

In this paper, we propose an optimization framework for the powertrain design of a two-wheel-driven electric superbike, minimizing energy consumption. Specifically, we jointly optimize the force distribution between the wheels with the gear ratio, and rear motor and battery sizing while explicitly considering vehicle dynamics and performance constraints. First, we present an energy consumption model of the vehicle, including a scalable model of the electric machine based on data from the industry, accounting for iron, copper, and mechanical losses. Then, we analyze the propulsive blending strategy to distribute the required power to the wheels while considering adherence limits. Finally, we demonstrate the effectiveness of our approach by analyzing the design of a superbike, based on regulatory driving cycles and a custom high-performance circuit by comparing the force distribution approaches. The results underline the significance of joint optimization of powertrain components and propulsive bias, achieving a reduction of up to 22.36% in energy consumption for the Sport high-performance driving cycle.
Privacy Preservation for Statistical Input in Dynamical Systems
Authors: Le Liu, Yu Kawano, Ming Cao
Date: 2025-03-31 00:00:00

This paper addresses the challenge of privacy preservation for statistical inputs in dynamical systems. Motivated by an autonomous building application, we formulate a privacy preservation problem for statistical inputs in linear time-invariant systems. What makes this problem widely applicable is that the inputs, rather than being assumed to be deterministic, follow a probability distribution, inherently embedding privacy-sensitive information that requires protection. This formulation also presents a technical challenge as conventional differential privacy mechanisms are not directly applicable. Through rigorous analysis, we develop strategy to achieve $(0, \delta)$ differential privacy through adding noise. Finally, the effectiveness of our methods is demonstrated by revisiting the autonomous building application.
Surveying Uncertainty Representation: A Unified Model for Cyber-Physical Systems
Authors: Johannes Mäkelburg, Diego Perez-Palacin, Raffaela Mirandola, Maribel Acosta
Date: 2025-03-31 00:00:00

Cyber-Physical Systems (CPS) operate in dynamic environments, leading to different types of uncertainty. This work provides a comprehensive review of uncertainty representations and categorizes them based on the dimensions used to represent uncertainty. Through this categorization, key gaps and limitations in existing approaches are identified. To address these issues, a Conceptual Model of Uncertainty Representations in CPS is introduced, integrating and extending existing models. Its applicability is demonstrated through examples from the automotive domain, showing its effectiveness in capturing and structuring uncertainty in real-world scenarios.
Robust Suboptimal Local Basis Function Algorithms for Identification of Nonstationary FIR Systems in Impulsive Noise Environments
Authors: Maciej Niedźwiecki, Artur Gańcza, Wojciech Żuławiński, Agnieszka Wyłomańska
Date: 2025-03-31 00:00:00

While local basis function (LBF) estimation algorithms, commonly used for identifying/tracking systems with time-varying parameters, demonstrate good performance under the assumption of normally distributed measurement noise, the estimation results may significantly deviate from satisfactory when the noise distribution is impulsive in nature, for example, corrupted by outliers. This paper introduces a computationally efficient method to make the LBF estimator robust, enhancing its resistance to impulsive noise. First, the choice of basis functions is optimized based on the knowledge of parameter variation statistics. Then, the parameter tracking algorithm is made robust using the sequential data trimming technique. Finally, it is demonstrated that the proposed algorithm can undergo online tuning through parallel estimation and leave-one-out cross-validation.
Incremental capacity-based multi-feature fusion model for predicting state-of-health of lithium-ion batteries
Authors: Chenyu Jia, Guangshu Xia, Yuanhao Shi, Jianfang Jia, Jie Wen, Jianchao Zeng
Date: 2025-03-31 00:00:00

Lithium-ion batteries have become an indispensable part of human industrial production and daily life. For the safe use, management and maintenance of lithium-ion batteries, the state of health (SOH) of lithium-ion batteries is an important indicator so that the SOH estimation is of significant practical value. In order to accurately predict SOH, this paper proposes a fusion prediction model which combines particle swarm optimization (PSO) algorithm, bi-directional long-short time memory network (BiLSTM) and adaptive boosting (AdaBoost) algorithm. In the proposed prediction model, indirect health indicators (HIs), which characterize battery degradation, are obtained with the help of incremental capacity analysis (ICA), and is fed into BiLSTM to extract time-series features, whose parameters are optimized by employing PSO algorithm. On this basis, the AdaBoost algorithm is applied to reduce the risk of overfitting the PSO-BiLSTM model. The study based on lithium-ion battery data from Center for Advanced Life Cycle Engineering (CALCE) shows that the PSO-BiLSTM-AdaBoost model has higher accuracy, better robustness, and generalization ability.
On the Analysis of Qualitative Nyquist Plots
Authors: Davide Tebaldi, Roberto Zanasi
Date: 2025-03-31 00:00:00

A powerful tool in control and systems engineering is represented by Nyquist plots, for which a qualitative representation often gives a clearer visualization of the frequency response function that is typically not given by computer programs, especially if portions of the Nyquist plot extend to infinity. This letter addresses the graphical analysis of the frequency response function, with the objective of enhancing the procedure for the qualitative construction of Nyquist plots. Several results supported by analytical proofs are derived for what concerns the low and high frequency behavior, which enable to improve the qualitative construction of Nyquist plots in the vicinity of the initial and final points.