Audio and Speech Processing
Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement
Nicolás Arrieta Larraza and Niels de Koeijer
Single-channel
speech enhancement
algorithms are often used in resource-constrained embedded devices, where low latency and low complexity designs gain more importance. In recent years, researchers have proposed a wide variety of novel solutions to this problem. In particular, a recent deep learning model named ULCNet is among the state-of-the-art approaches in this domain. This paper proposes an adaptation of ULCNet, by replacing its GRU layers with FastGRNNs, to reduce both computational latency and complexity. Furthermore, this paper shows empirical evidence on the performance decay of FastGRNNs in long audio signals during inference due to internal state drifting, and proposes a novel approach based on a trainable complementary filter to mitigate it. The resulting model, Fast-ULCNet, performs on par with the state-of-the-art original ULCNet architecture on a
speech enhancement task, while reducing its model size by more than half and decreasing its latency by 34% on average.
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Systems and Control · Cross-listed from math.OC
Practical prescribed-time prescribed performance control with asymptotic convergence – A vanishing sigma-modification approach
Mehdi Golestani, Yongduan Song, Weizhen Liu et al. (+2 more)
In this paper, we present a method capable of ensuring practical prescribed-time control with guaranteed performance for a class of nonlinear systems in the presence of time-varying parametric and dynamic uncertainties, and uncertain control coefficients. Our design consists of two key steps. First, we construct a performance-rate function that freezes at and after a user-specified time T, playing a crucial role in achieving desired precision within prescribed time T and dealing with unmodeled dynamics. Next, based on this function and a sigma-modification strategy in which the leakage term starts to vanish at t > T, we develop an adaptive dynamic surface control framework to reduce control complexity, deal with uncertainties, ensure prescribed performance, practical prescribed-time convergence to a specific region, and ultimately achieve asymptotic convergence. The effectiveness of the proposed control method is validated through numerical simulations.
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All authors Mehdi Golestani, Yongduan Song, Weizhen Liu, Guangren Duan, He Kong
Signal Processing
Analysis of Sensing in OFDM-based ISAC under the Influence of Sampling Jitter
Lucas Giroto, Ândrei Camponogara, Yueheng Li et al. (+4 more)
To enable integrated sensing and communication (
ISAC) in cellular networks, a wide range of additional requirements and challenges are either imposed or become more critical. One such impairment is sampling jitter (SJ), which arises due to imperfections in the sampling instants of the clocks of digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). While SJ is already well studied for communication systems based on orthogonal frequency-division multiplexing (OFDM), which is expected to be the waveform of choice for most sixth-generation (6G) scenarios where
ISAC could be possible, the implications of SJ on the OFDM-based radar sensing must still be thoroughly analyzed. Considering that phase-locked loop (PLL)-based oscillators are used to derive sampling clocks, which leads to colored SJ, i.e., SJ with non-flat power spectral density, this article analyzes the resulting distortion of the adopted digital constellation modulation and sensing performance in OFDM-based
ISAC for both baseband (BB) and bandpass (BP) sampling strategies and different oversampling factors. For BB sampling, it is seen that SJ induces intercarrier interference (ICI), while for BP sampling, it causes carrier phase error and more severe ICI due to a phase noise-like effect at the digital intermediate frequency. Obtained results for a single-input single-output OFDM-based
ISAC system with various OFDM signal parameterizations demonstrate that SJ-induced degradation becomes non-negligible for both BB and BP sampling only for root mean square (RMS) SJ values above 10^-11 s at both DAC and ADC, which corresponds to 0.5*10^-2 times the considered critical sampling period without oversampling. Based on the achieved results, it can be concluded that state-of-the-art hardware enables sufficient communication and sensing robustness against SJ, as RMS SJ values in the femtosecond range can be achieved.
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All authors Lucas Giroto, Ândrei Camponogara, Yueheng Li, Jiayi Chen, Lukas Sigg, Thomas Zwick, Benjamin Nuss
Systems and Control
Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives
Lei Zheng, Luyao Zhang, Peiqi Yu et al. (+4 more)
Contingency planning is the architectural capability that enables autonomous vehicles (AVs) to anticipate and mitigate discrete, high-impact hazards, such as sensor outages and
adversarial interactions. This paper presents a comprehensive survey of the field, synthesizing fragmented literature into a unified logic-conditioned hybrid control framework. Within this formalism, we categorize approaches into two distinct paradigms: Reactive Safety, which responds to realized hazards by enforcing safety constraints or executing fail-safe maneuvers; and Proactive Safety, which optimizes for future recourse by branching over potential modal transitions. In addition, we propose a fine-grained taxonomy that partitions the landscape into external contingencies (environmental and interactive hazards) and internal contingencies (system faults). Through a critical comparative analysis, we reveal a fundamental structural divergence: internal faults are predominantly addressed via reactive fail-safe mechanisms, whereas external interaction uncertainties increasingly require proactive branching strategies. Furthermore, we identify a critical methodological divergence: whereas physical hazards are typically managed with formal guarantees, semantic and out-of-distribution anomalies currently rely heavily on empirical validation. We conclude by identifying the open challenges in bridging the gap between theoretical guarantees and practical validation, advocating for hybrid architectures and standardized benchmarking to transition contingency planning from formulation to certifiable real-world deployment.
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All authors Lei Zheng, Luyao Zhang, Peiqi Yu, Yifan Sun, Sergio Grammatico, Jun Ma, Changliu Liu
Signal Processing
Movable Antenna Empowered Covert Dual-Functional Radar-Communication
Ran Yang, Ning Wei, Zheng Dong et al. (+5 more)
Movable
antenna (MA) has emerged as a promising technology to flexibly reconfigure wireless channels by adjusting
antenna placement. In this paper, we study a secured dual-functional radar-communication (DFRC) system aided by movable
antennas. To enhance the communication security, we aim to maximize the achievable sum rate by jointly optimizing the transmitter beamforming vectors, receiving filter, and
antenna placement, subject to radar signal-to-noise ratio (SINR) and transmission covertness constraints. We consider multiple Willies operating in both non-colluding and colluding modes. For noncolluding Willies, we first employ a Lagrangian dual transformation procedure to reformulate the challenging optimization problem into a more tractable form. Subsequently, we develop an efficient block coordinate descent (BCD)
algorithm that integrates semidefinite relaxation (SDR), projected gradient descent (PGD), Dinkelbach transformation, and successive convex approximation (SCA) techniques to tackle the resulting problem. For colluding Willies, we first derive the minimum detection error probability (DEP) by characterizing the optimal detection statistic, which is proven to follow the generalized Erlang distribution. Then, we develop a minimum mean square error (MMSE)-based
algorithm to address the colluding detection problem. We further provide a comprehensive complexity analysis on the unified design framework. Simulation results demonstrate that the proposed method can significantly improve the covert sum rate, and achieve a superior balance between communication and radar performance compared with existing
benchmark schemes.
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All authors Ran Yang, Ning Wei, Zheng Dong, Lin Zhang, Wanting Lyu, Yue Xiu, Ahmad Bazzi, Chadi Assi
Signal Processing
Absorption mode broadband 2D MS for proteomics and metabolomics
Maria A van Agthoven, Marek Polák, Jan Fiala et al. (+9 more)
Two-dimensional mass spectrometry (2D MS) is a method for tandem mass spectrometry that enables the correlation between precursor and fragment ions without the need for ion isolation. On a
Fourier transform ion cyclotron resonance mass spectrometer, the phase correction functions for absorption mode data processing were found to be linear in the precursor ion dimension and quadratic in the fragment ion dimension. Absorption mode data processing on limited data sets has previously shown improvements in signal-to-noise ratio and resolving power by a factor of 2. Here, we have expanded absorption mode data processing to 2D mass spectra regardless of size and frequency range. We have applied absorption mode 2D MS to top-down analysis of variously oxidized ubiquitin proteoforms generated by fast photochemical oxidation of
proteins (FPOP) and to an extract of ergot alkaloids. We show that absorption mode data processing significantly improves both the signal-to-noise ratio and the resolving power of the 2D mass spectrum compared to standard magnitude mode in terms of sequence coverage in top-down proteomics, as well as the accuracy of precursor-fragment correlation in metabolomics.
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All authors Maria A van Agthoven, Marek Polák, Jan Fiala, Claude Nelcy Ounounou, Petr Halada, Michael Palasser, Anne Briot-Dietsch, Alan Kádek, Kathrin Breuker, Petr Novák, Carlos Afonso, Marc-André Delsuc
Image and Video Processing
LiNUS: Lightweight Automatic Segmentation of Deep Brain Nuclei for Real-Time DBS Surgery
Shuo Zhang, Zihua Wang, Changgeng He et al. (+1 more)
This paper proposes LiNUS, a lightweight deep learning framework for the automatic
segmentation of the Subthalamic
Nucleus (STN) in Deep
Brain Stimulation (DBS) surgery. Addressing the challenges of small target volume and class imbalance in MRI data, LiNUS improves upon the U-Net architecture by introducing spectral normalization constraints, bilinear interpolation upsampling, and a multi-scale feature fusion mechanism. Experimental results on the Tsinghua DBS
dataset (TT14) demonstrate that LiNUS achieves a Dice coefficient of 0.679 with an inference time of only 0.05 seconds per subject, significantly outperforming traditional manual and registration-based methods. Further validation on high-resolution data confirms the model's robustness, achieving a Dice score of 0.89. A dedicated Graphical User Interface (GUI) was also developed to facilitate real-time clinical application.
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All authors Shuo Zhang, Zihua Wang, Changgeng He, Chunhua Hu
Signal Processing
Integrated Sensing, Communication and Control enabled Agile UAV Swarm
Zhiqing Wei, Yucong Du, Zhiyong Feng et al. (+5 more)
Uncrewed aerial vehicle (
UAV) swarms are pivotal in the applications such as disaster relief, aerial base station (BS) and logistics transportation. These scenarios require the capabilities in accurate sensing, efficient communication and flexible control for real-time and reliable task execution. However, sensing, communication and control are studied independently in traditional research, which limits the overall performance of
UAV swarms. To overcome this disadvantage, we propose a deeply coupled scheme of integrated sensing, communication and control (ISCC) for
UAV swarms, which is a systemic paradigm that transcends traditional isolated designs of sensing, communication and control by establishing a tightly-coupled closed-loop through the co-optimization of sensing, communication and control. In this article, we firstly analyze the requirements of scenarios and key performance metrics. Subsequently, the enabling technologies are proposed, including communication-and-control-enhanced sensing, sensing-and-control-enhanced communication, and sensing-and-communication-enhanced control. Simulation results validate the performance of the proposed ISCC framework, demonstrating its application potential in the future.
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All authors Zhiqing Wei, Yucong Du, Zhiyong Feng, Haotian Liu, Yanpeng Cui, Tao Zhang, Ying Zhou, Huici Wu
Audio and Speech Processing
Test-Time Adaptation For Speech Enhancement Via Mask Polarization
Tobias Raichle, Erfan Amini and Bin Yang
Adapting
speech enhancement (SE) models to unseen environments is crucial for practical deployments, yet test-time adaptation (TTA) for SE remains largely under-explored due to a lack of understanding of how SE models degrade under domain shifts. We observe that mask-based SE models lose confidence under domain shifts, with predicted masks becoming flattened and losing decisive
speech preservation and noise suppression. Based on this insight, we propose mask polarization (MPol), a lightweight TTA method that restores mask bimodality through distribution comparison using the Wasserstein distance. MPol requires no additional parameters beyond the trained model, making it suitable for resource-constrained edge deployments. Experimental results across diverse domain shifts and architectures demonstrate that MPol achieves very consistent gains that are competitive with significantly more complex approaches.
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Systems and Control · Cross-listed from quant-ph
Blended Dynamics and Emergence in Open Quantum Networks
Qinghao Wen, Zihao Ren, Lei Wang et al. (+2 more)
In this paper, we develop a blended dynamics framework for open
quantum networks with diffusive couplings. The network consists of
qubits interconnected through
Hamiltonian couplings, environmental dissipation, and consensus-like diffusive interactions. Such networks commonly arise in spontaneous emission processes and non-Hermitian
quantum computing, and their
evolution follows a Lindblad master equation. Blended dynamics theory is well established in the classical setting as a tool for analyzing emergent behaviors in heterogeneous networks with diffusive couplings. Its key insight is to blend the local dynamics rather than the trajectories of individual nodes. Perturbation analysis then shows that, under sufficiently strong coupling, all node trajectories tend to stay close to those of the blended system over time. We first show that this theory extends naturally to the reduced-state dynamics of
quantum networks, revealing classical-like clustering phenomena in which
qubits converge to a shared
equilibrium or a common trajectory determined by the
quantum blended reduced-state dynamics. We then extend the analysis to
qubit coherent states using
quantum Laplacians and induced graphs, proving orbit attraction of the network density operator toward the
quantum blended coherent dynamics, establishing the emergence of intrinsically
quantum and dynamically clustering behaviors. Finally, numerical examples validate the theoretical results.
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All authors Qinghao Wen, Zihao Ren, Lei Wang, Hyungbo Shim, Guodong Shi
Signal Processing
Improved GPR-Based CSI Acquisition via Spatial-Correlation Kernel
Syed Luqman Shah, Nurul Huda Mahmood and Italo Atzeni
Accurate channel estimation with low pilot overhead and computational complexity is key to efficiently utilizing multi-
antenna wireless systems. Motivated by the
evolution from purely statistical descriptions toward physics- and geometry-aware propagation models, this work focuses on incorporating channel information into a Gaussian process regression (GPR) framework for improving the channel estimation accuracy. In this work, we propose a GPR-based channel estimation framework along with a novel Spatial-correlation (SC) kernel that explicitly captures the channel's second-order statistics. We derive a closed-form expression of the proposed SC-based GPR
estimator and prove that its posterior mean is optimal in terms of minimum mean-square error (MMSE) under the same second-order statistics, without requiring the underlying channel distribution to be Gaussian. Our analysis reveals that, with up to 50% pilot overhead reduction, the proposed method achieves the lowest normalized mean-square error, the highest empirical 95% credible-interval coverage, and superior preservation of spectral efficiency compared to
benchmark estimators, while maintaining lower computational complexity than the conventional MMSE
estimator.
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Audio and Speech Processing
Inverse-Hessian Regularization for Continual Learning in ASR
Steven Vander Eeckt and Hugo Van hamme
Catastrophic forgetting remains a major challenge for continual learning (CL) in automatic
speech recognition (ASR), where models must adapt to new domains without losing performance on previously learned conditions. Several CL methods have been proposed for ASR, and, recently, weight averaging - where models are averaged in a merging step after fine-tuning - has proven effective as a simple
memory-free strategy. However, it is heuristic in nature and ignores the underlying loss landscapes of the tasks, hindering adaptability. In this work, we propose Inverse Hessian Regularization (IHR), a
memory-free approach for CL in ASR that incorporates
curvature information into the merging step. After fine-tuning on a new task, the adaptation is adjusted through a Kronecker-factored inverse Hessian approximation of the previous task, ensuring that the model moves primarily in directions less harmful to past performance, while keeping the method lightweight. We evaluate IHR on two CL
benchmarks and show that it significantly outperforms state-of-the-art baselines, reducing forgetting while improving adaptability. Ablation studies and analyses further confirm its effectiveness.
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Audio and Speech Processing · Cross-listed from cs.SD
Unlocking Large Audio-Language Models for Interactive Language Learning
Hongfu Liu, Zhouying Cui, Xiangming Gu et al. (+1 more)
Achieving pronunciation proficiency in a second
language (L2) remains a challenge, despite the development of Computer-Assisted Pronunciation Training (CAPT) systems. Traditional CAPT systems often provide unintuitive feedback that lacks actionable guidance, limiting its effectiveness. Recent advancements in audio-
language models (ALMs) offer the potential to enhance these systems by providing more user-friendly feedback. In this work, we investigate ALMs for chat-based pronunciation training by introducing L2-Arctic-plus, an English
dataset with detailed error explanations and actionable suggestions for improvement. We
benchmark cascaded ASR+LLMs and existing ALMs on this
dataset, specifically in detecting mispronunciation and generating actionable feedback. To improve the performance, we further propose to instruction-tune ALMs on L2-Arctic-plus. Experimental results demonstrate that our instruction-tuned models significantly outperform existing baselines on mispronunciation detection and suggestion generation in terms of both objective and human evaluation, highlighting the value of the proposed
dataset.
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All authors Hongfu Liu, Zhouying Cui, Xiangming Gu, Ye Wang
Audio and Speech Processing
AQAScore: Evaluating Semantic Alignment in Text-to-Audio Generation via Audio Question Answering
Chun-Yi Kuan, Kai-Wei Chang and Hung-yi Lee
Although text-to-audio generation has made remarkable progress in realism and diversity, the development of evaluation metrics has not kept pace. Widely-adopted approaches, typically based on
embedding similarity like CLAPScore, effectively measure general relevance but remain limited in fine-grained semantic
alignment and compositional
reasoning. To address this, we introduce AQAScore, a backbone-agnostic evaluation framework that leverages the
reasoning capabilities of audio-aware large
language models (ALLMs). AQAScore reformulates assessment as a probabilistic semantic verification task; rather than relying on open-ended text generation, it estimates
alignment by computing the exact log-probability of a "Yes" answer to targeted semantic queries. We evaluate AQAScore across multiple
benchmarks, including human-rated relevance, pairwise comparison, and compositional
reasoning tasks. Experimental results show that AQAScore consistently achieves higher correlation with human judgments than similarity-based metrics and generative prompting baselines, showing its effectiveness in capturing subtle semantic inconsistencies and scaling with the capability of underlying ALLMs.
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Systems and Control
Differential Privacy on Affine Manifolds: Geometrically Confined Privacy in Linear Dynamical Systems
Zihao Ren, Lei Wang, Deming Yuan et al. (+1 more)
In this paper, we present a comprehensive framework for differential
privacy over affine
manifolds and validate its usefulness in the contexts of differentially private cloud-based control and average consensus. We consider differential
privacy mechanisms for linear queries when the input data are constrained to lie on affine
manifolds, a structural property that is assumed to be available as prior knowledge to adversaries. In this setting, the definition of neighborhood adjacency must be formulated with respect to the intrinsic geometry of the
manifolds. We demonstrate that such affine-
manifold constraints can fundamentally alter the attainable
privacy levels relative to the unconstrained case. In particular, we derive necessary and sufficient conditions under which differential
privacy can be realized via structured noise injection mechanisms, wherein correlated Gaussian or Laplace noise distributions, rather than i.i.d. perturbations, are calibrated to the
dataset. Based on these characterizations, we develop explicit noise calibration procedures that guarantee the tight realization of any prescribed
privacy budget with a matching noise magnitude. Finally, we show that the proposed framework admits direct applications to linear dynamical systems ranging from differentially private cloud-based control to
privacy-preserving average consensus, all of which naturally involve affine-
manifold constraints. The established theoretical results are illustrated through numerical examples.
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All authors Zihao Ren, Lei Wang, Deming Yuan, Guodong Shi
Audio and Speech Processing
NLP-Based Review for Toxic Comment Detection Tailored to the Chinese Cyberspace
Ruixing Ren, Junhui Zhao, Xiaoke Sun et al. (+1 more)
With the in-depth integration of mobile Internet and widespread adoption of social platforms, user-generated content in the Chinese cyberspace has witnessed explosive growth. Among this content, the proliferation of toxic comments poses severe challenges to individual mental health, community atmosphere and social trust. Owing to the strong context dependence, cultural specificity and rapid
evolution of Chinese cyber
language, toxic expressions are often conveyed through complex forms such as homophones and metaphors, imposing notable limitations on traditional detection methods. To address this issue, this review focuses on the core topic of natural
language processing based toxic comment detection in the Chinese cyberspace, systematically collating and critically analyzing the research progress and key challenges in this field. This review first defines the connotation and characteristics of Chinese toxic comments, and analyzes the platform ecology and transmission mechanisms they rely on. It then comprehensively reviews the construction methods and limitations of existing public
datasets, and proposes a novel fine-grained and scalable framework for toxic comment definition and classification, along with corresponding data annotation and quality assessment strategies. We systematically summarize the evolutionary path of detection models from traditional methods to deep learning, with special emphasis on the importance of interpretability in model design. Finally, we thoroughly discuss the open challenges faced by current research and provide forward-looking suggestions for future research directions.
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All authors Ruixing Ren, Junhui Zhao, Xiaoke Sun, Qiuping Li
Systems and Control
Hierarchical Optimization Based Multi-objective Dynamic Regulation Scheme for VANET Topology
Ruixing Ren, Minqi Tao, Junhui Zhao et al. (+2 more)
As a core technology of intelligent transportation systems, vehicular ad-hoc networks support latency-sensitive services such as safety warning and cooperative perception via vehicle-to-everything communications. However, their highly dynamic
topology increases average path length, raises latency, and reduces throughput, severely limiting communication performance. Existing
topology optimization methods lack capabilities in multi-objective coordination, dynamic adaptation, and global-local synergy. To address this, this paper proposes a two-layer dynamic
topology regulation scheme combining local feature aggregation and global adjustment. The scheme constructs a dynamic multi-objective optimization model integrating average path length, end-to-end latency, and network throughput, and achieves multi-index coordination via link adaptability metrics and a dynamic normalization mechanism. it quickly responds to local link changes via feature fusion of local node feature extraction and dynamic neighborhood sensing, and balances optimization accuracy and real-time performance using a dual-mode adaptive solving strategy for global
topology adjustment. It reduces network oscillation risks by introducing a performance improvement threshold and a
topology validity verification mechanism. Simulation results on real urban road networks via the SUMO platform show that the proposed scheme outperforms traditional methods in average path length (stabilizing at ~4 hops), end-to-end latency (remaining ~0.01 s), and network throughput.
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All authors Ruixing Ren, Minqi Tao, Junhui Zhao, Xiaoke Sun, Qiuping Li
Audio and Speech Processing
Triage knowledge distillation for speaker verification
Ju-ho Kim, Youngmoon Jung, Joon-Young Yang et al. (+3 more)
Deploying speaker verification on resource-constrained devices remains challenging due to the computational cost of high-capacity models; knowledge
distillation (KD) offers a remedy. Classical KD entangles target confidence with non-target structure in a Kullback-Leibler term, limiting the transfer of relational information. Decoupled KD separates these signals into target and non-target terms, yet treats non-targets uniformly and remains vulnerable to the long tail of low-probability classes in large-class settings. We introduce Triage KD (TRKD), a
distillation scheme that operationalizes assess-prioritize-focus. TRKD introduces a cumulative-probability cutoff $\tau$ to assess per-example difficulty and partition the teacher posterior into three groups: the target class, a high-probability non-target confusion-set, and a background-set. To prioritize informative signals, TRKD distills the confusion-set conditional distribution and discards the background. Concurrently, it transfers a three-mass (target/confusion/background) that capture sample difficulty and inter-class confusion. Finally, TRKD focuses learning via a curriculum on $\tau$: training begins with a larger $\tau$ to convey broad non-target context, then $\tau$ is progressively decreased to shrink the confusion-set, concentrating supervision on the most confusable classes. In extensive experiments on VoxCeleb1 with both homogeneous and heterogeneous teacher-student pairs, TRKD was consistently superior to recent KD variants and attained the lowest EER across all protocols.
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All authors Ju-ho Kim, Youngmoon Jung, Joon-Young Yang, Jaeyoung Roh, Chang Woo Han, Hoon-Young Cho
Systems and Control
Ramping-aware Enhanced Flexibility Aggregation of Distributed Generation with Energy Storage in Power Distribution Networks
Hyeongon Park, Daniel K. Molzahn and Rahul K. Gupta
Power distribution networks are increasingly hosting controllable and flexible distributed energy resources (DERs) that, when aggregated, can provide ancillary support to transmission systems. However, existing aggregation schemes often ignore the ramping constraints of these DERs, which can render them impractical in real deployments. This work proposes a ramping-aware flexibility aggregation scheme, computed at the transmission-distribution boundary, that explicitly accounts for DER ramp limits and yields flexibility envelopes that are provably disaggregable. To further enhance the attainable flexibility region, we introduce a novel pre-ramping strategy, which proactively adjusts resource operating points to enlarge the aggregated flexibility envelope while preserving both network feasibility and disaggregation guarantees. The proposed method demonstrates a 5.2% to 19.2% improvement in flexibility relative to the baseline model, depending on system conditions. We validate the scheme on an IEEE-33 bus distribution system and provide formal proofs showing that both aggregation strategies are disaggregable for all feasible trajectories within the aggregate flexibility envelope.
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Systems and Control
Efficient reformulations of ReLU deep neural networks for surrogate modelling in power system optimisation
Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A.R. Liisberg et al. (+1 more)
The ongoing decarbonisation of power systems is driving an increasing reliance on distributed energy resources, which introduces complex and nonlinear interactions that are difficult to capture in conventional optimisation models. As a result, machine learning based surrogate modelling has emerged as a promising approach, but integrating machine learning models such as ReLU deep neural networks (DNNs) directly into optimisation often results in nonconvex and computationally intractable formulations. This paper proposes a linear programming (LP) reformulation for a class of convexified ReLU DNNs with non-negative weight
matrices beyond the first layer, enabling a tight and tractable
embedding of learned surrogate models in optimisation. We evaluate the method using a case study on learning the prosumer's responsiveness within an aggregator bidding problem in the Danish tertiary capacity
market. The proposed reformulation is benchmarked against state-of-the-art alternatives, including piecewise linearisation (PWL), MIP-based
embedding, and other LP relaxations. Across multiple neural network architectures and
market scenarios, the convexified ReLU DNN achieves solution quality comparable to PWL and MIP-based reformulations while significantly improving computational performance and preserving model fidelity, unlike penalty-based reformulations. The results demonstrate that convexified ReLU DNNs offer a scalable and reliable methodology for integrating learned surrogate models in optimisation, with applicability to a wide range of emerging power system applications.
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All authors Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A.R. Liisberg, Julian Lesmos-Vinasco
Systems and Control
Risk-Averse Power System Resilience Planning Under AI Data Center Demand Growth Using a Two-Stage DRO-CVaR MILP Optimization
Sharaf K. Magableh, Caisheng Wang, Oraib Dawaghreh et al. (+1 more)
The rapid growth of artificial intelligence-data centers is introducing significant demand variability and operational uncertainty into modern power systems, creating new challenges for resilience planning and grid operation. In addition to physical disruptions such as line outages and uncertain restoration processes, AI-driven loads can exhibit highly dynamic behavior. This paper presents a two-stage risk-averse Distributionally Robust Optimization (DRO)-Mixed Integer Linear Programming framework for enhancing distribution-system resilience under combined AI-driven demand uncertainty and physical disruptions. The proposed framework coordinates the strategic prepositioning and adaptive dispatch of flexible capacity modules, including battery energy storage systems and mobile diesel generation resources. A unified scenario representation captures AI-driven demand variability, line outages, and repair-time uncertainty. To improve robustness against uncertainty misspecification and extreme events, the model integrates Conditional Value-at-Risk within a DRO formulation. Validation on the IEEE 33-bus distribution system demonstrates reductions in Energy Not Supplied, improved served-load recovery, enhanced restoration performance, and increased system resilience under severe operating conditions.
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All authors Sharaf K. Magableh, Caisheng Wang, Oraib Dawaghreh, Xuesong Wang
Systems and Control
Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets
Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A.R. Liisberg et al. (+1 more)
Reliable
forecasting of prosumer flexibility is critical for demand response aggregators in frequency controlled ancillary service
markets, where reliability requirements such as the P90 standard are enforced. Limited behavioural data and dependence on exogenous forecasts introduce uncertainty into flexibility estimates, which can cause reserve overestimation and financial penalties. This study systematically evaluates established uncertainty quantification approaches based on Monte Carlo dropout (MCD), conformal prediction (CP), and conformalised
quantile regression (CQR) to construct prediction intervals for aggregated prosumer flexibility and support uncertainty-aware bidding. The approaches are evaluated for a behind-the-meter aggregator in the Danish manual frequency restoration reserve capacity
market, using a large-scale synthetic
dataset generated with an industry-grade home energy management system and publicly available load,
solar, price, activation, and device-level data. Multiple multivariate CP strategies are applied to MCD predictions, alongside CQR using a separately trained
quantile regression model. Under nominal test conditions, conformal methods substantially improve joint coverage over uncalibrated MCD baselines. More conservative methods satisfy the 90% lower-tail reliability requirement, while CQR and multivariate CP (MCP) yield narrower intervals and higher economic value, retaining approximately 69% and 68% of perfect-information profit, respectively, under the stricter penalty interpretation. A controlled robustness analysis shows that increasing perturbations of load,
solar, price, and activation inputs increase overbidding and reduce coverage and economic performance. The results highlight the trade-off between reliability, economic value, and robustness to input uncertainty in aggregator bidding.
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All authors Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A.R. Liisberg, Julian Lemos-Vinasco
Signal Processing
Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-free Massive MIMO Systems with Reciprocity Calibration
Qingji Jiang, Jing jin, Qixing Wang et al. (+9 more)
Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave)
cell-free massive multiple-input and multiple-output (
MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.
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All authors Qingji Jiang, Jing jin, Qixing Wang, Yuanyuan Tang, Yang Cao, Bin Kuang, Jing Dong, Siying Lv, Dongming Wang, Yongming Huang, Jiangzhou Wang, Xiaohu You
Systems and Control
Input-to-State Stabilizing Neural Controllers for Unknown Switched Nonlinear Systems within Compact Sets
Bhabani Shankar Dey, Ahan Basu and Pushpak Jagtap
This paper develops a neural network based control framework that ensures system safety and input-to-state stability (ISS) for general nonlinear switched systems with unknown dynamics. Leveraging the concept of dwell time, we derive Lyapunov based sufficient conditions under which both safety and ISS of the closed-loop switched system are guaranteed. The feedback controllers and the associated Lyapunov functions are parameterized using neural networks and trained from data collected over a compact state space via deterministic sampling. To provide formal stability guarantees under the learned controllers, we introduce a validity condition based on Lipschitz continuity assumptions, which is embedded directly into the training framework. This ensures that the resulting neural network controllers satisfy provable correctness and stability guarantees beyond the sampled data. As a special case, the proposed framework recovers ISS and safety under arbitrary switching when a common Lyapunov function exists. Simulation results on a representative switched nonlinear system demonstrate the effectiveness of the proposed approach.
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Systems and Control · Cross-listed from cs.DC
Specifying and Verifying RDMA Synchronisation (Extended Version)
Guillaume Ambal, Max Stupple, Brijesh Dongol et al. (+1 more)
Remote direct
memory access (RDMA) allows a machine to directly read from and write to the
memory of remote machine, enabling high-throughput, low-latency data transfer. Ensuring correctness of RDMA programs has only recently become possible with the formalisation of $\text{RDMA}^\text{TSO}$ semantics (describing the behaviour of RDMA networking over a TSO CPU). However, this semantics currently lacks a formalisation of remote synchronisation, meaning that the implementations of common abstractions such as locks cannot be verified. In this paper, we close this gap by presenting $\text{RDMA}^{\text{TSO}}_{\text{RMW}}$, the first semantics for remote `read-modify-write' (RMW) instructions over TSO. It turns out that remote RMW operations are weak and only ensure atomicity against other remote RMWs. We therefore build a set of composable synchronisation abstractions starting with the $\text{RDMA}^{\text{WAIT}}_{\text{RMW}}$ library. Underpinned by $\text{RDMA}^{\text{WAIT}}_{\text{RMW}}$, we then specify, implement and verify three classes of remote locks that are suitable for different scenarios. Additionally, we develop the notion of a strong RDMA model, $\text{RDMA}^{\text{SC}}_{\text{RMW}}$, which is akin to sequential consistency in shared
memory architectures. Our libraries are built to be compatible with an existing set of high-performance libraries called LOCO, which ensures compositionality and verifiability.
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All authors Guillaume Ambal, Max Stupple, Brijesh Dongol, Azalea Raad
Audio and Speech Processing
Scaling Ambiguity: Augmenting Human Annotation in Speech Emotion Recognition with Audio-Language Models
Wenda Zhang, Hongyu Jin, Siyi Wang et al. (+2 more)
Speech Emotion Recognition models typically use single categorical labels, overlooking the inherent ambiguity of human
emotions. Ambiguous
Emotion Recognition addresses this by representing
emotions as probability distributions, but progress is limited by unreliable ground-truth distributions inferred from sparse human annotations. This paper explores whether Large Audio-
Language Models (ALMs) can mitigate the annotation bottleneck by generating high-quality synthetic annotations. We introduce a framework leveraging ALMs to create Synthetic Perceptual Proxies, augmenting human annotations to improve ground-truth distribution reliability. We validate these proxies through statistical analysis of their
alignment with human distributions and evaluate their impact by fine-tuning ALMs with the augmented
emotion distributions. Furthermore, to address class imbalance and enable unbiased evaluation, we propose DiME-Aug, a Distribution-aware
Multimodal Emotion Augmentation strategy. Experiments on IEMOCAP and MSP-Podcast show that synthetic annotations enhance
emotion distribution, especially in low-ambiguity regions where annotation agreement is high. However, benefits diminish for highly ambiguous
emotions with greater human disagreement. This work provides the first evidence that ALMs could address annotation scarcity in ambiguous
emotion recognition, but highlights the need for more advanced prompting or generation strategies to handle highly ambiguous cases.
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All authors Wenda Zhang, Hongyu Jin, Siyi Wang, Zhiqiang Wei, Ting Dang
Systems and Control
An Ion-Intercalation Memristor for Enabling Full Parallel Writing in Crossbar Networks
Tingwei Zhang, Jiahui Liu, David Allstot et al. (+1 more)
Crossbar architectures have long been seen as a promising foundation for in-
memory computing, using memristor arrays for high-density, energy-efficient analog computation. However, this conventional architecture suffers from a fundamental limitation: the inability to perform parallel write operations due to the sneak path problem. This arises from the structural overlap of read and write paths, forcing sequential or semi-parallel updates and severely limiting scalability. To address this, we introduce a new memristor design that decouples read and write operations at the device level. This design enables orthogonal conductive paths, and employs a reversible ion doping mechanism, inspired by lithium-ion battery principles, to modulate resistance states independently of computation. Fabricated devices exhibit near-ideal memristive characteristics and stable performance under isolated read/write conditions.
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All authors Tingwei Zhang, Jiahui Liu, David Allstot, Huaping Liu