Systems and Control · Cross-listed from math.OC
Dynamic Programming for A Linear-Gaussian Dual Control Problem
Ying Wang, Yuchao Li and Håkan Hjalmarsson
We consider an infinite-horizon discounted dynamic programming (DP) problem for a countable-innovation belief-state model. The model is a surrogate for a linear-Gaussian dual control problem involving an unknown parameter vector and Gaussian observation noise. The surrogate preserves the first two moments of the Gaussian innovation and the resulting posterior second-moment structure. We allow unbounded belief and control spaces and nonnegative stage costs with quadratic growth in the posterior mean and covariance. Using a posterior-moment weight, we show that the Bellman operator is a contraction in a weighted sup-norm space. We characterize the optimal cost as the unique solution of the Bellman equation in this space, establish the existence of optimal stationary policies. We further prove weighted sup-norm convergence of value iteration, policy iteration, and optimistic policy iteration under vanishing approximation errors. The convergence results under vanishing approximation errors rely on general weighted sup-norm contraction properties and are applicable beyond the particular dual control problem considered here.
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Systems and Control · Cross-listed from cs.AI
AgentStripes: An Evidence-Graded Maturity Model for AI Agents
Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj et al. (+2 more)
Benchmarks report what an Artificial Intelligence (AI)
agent can do on a task suite, but not whether one released version has been engineered with the controls that make it reliable, safe and observable, or how strongly that is shown. This paper proposes AgentStripes, an evidence-graded maturity model for AI
agents. It defines levels (0 to 7 stripes), process areas (12 areas holding 40 capabilities), three gates (basic, safety, proof) that hold an
agent at a level however high its score, and evidence grades E0 to E5. Each applicable capability receives a rating from 0 to 3 and a separate evidence level; these are combined into a credit, aggregated into a severity-weighted score and converted into stripes that the gates limit. Instruments gather evidence,
language-model judges interpret it under explicit criteria, and the arithmetic is deterministic. The model is compared with standards at topic level without a claim of conformance, and an unexecuted validation protocol is specified. An empirical evaluation (exploratory, non-confirmatory) of 30 public Python
agent repositories, with static evidence only, is a demonstration and not a validation: static evidence caps every final stripe at 2, 26 repositories end at one stripe and 4 at two, and this distinction is unreliable, since re-judging changed the final stripe of 4 of 22 repositories (engine, dossier and panel changed), whereas none of 8 panel-only repeats, all at stripe 1, changed. The parameters are expert conventions without outcome based calibration, and the stripes have not been shown to predict reliability or safety.
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All authors Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj, Saurav Samantray, Abhishek Patel
Audio and Speech Processing · Cross-listed from cs.SD
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition
Ting Zhu, Huizhi Liang, Boguslaw Obara et al. (+2 more)
Multimodal Emotion Recognition (MER) aims to understand human emotions by integrating information from multiple modalities such as
speech and text. Psychological studies suggest that emotions exhibit latent hierarchical structured relationships in their semantic representations. However, most MER approaches rely on Euclidean representations, which capture fine-grained cross-modal interactions but do not explicitly provide a
geometric prior for organizing hierarchical emotional structures. To mitigate this gap, we propose GeoMER, a joint Euclidean-
Hyperbolic modeling framework that introduces
hyperbolic emotion-core routing to capture higher-level, abstract emotional representations for MER. The proposed framework jointly models
token-level cross-modal semantic interactions and latent emotion representations in two complementary
geometric spaces. Specifically, GeoMER consists of a Euclidean
Multimodal Fusion Module (EuclFusion) for
multimodal representation fusion and a
Hyperbolic Geometric Core Routing (HypGeoRoute) module that performs geometry-aware core assignment and aggregation in a
hyperbolic manifold to capture more abstract feature representations. The HypGeoRoute module complements the Euclidean
token-level modeling. We experiment on the IEMOCAP
multimodal MER
benchmark and demonstrate that the proposed method achieves the best performance, with 83.54% weighted accuracy and 83.35% unweighted accuracy, outperforming Euclidean baselines, including MemoCMT.
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All authors Ting Zhu, Huizhi Liang, Boguslaw Obara, Shufei Duan, Varun Ojha
Systems and Control
Shadow Pricing in Transient Stability-Constrained Optimal Power Flow
Alex Coelho, Araceli Hernandez and Luis Badesa
Transient stability constraints alter the economic dispatch of power systems, while the resulting price signals and their implications for
market settlement remain insufficiently characterized. Existing approaches identify stability-related components in locational marginal prices, without deriving shadow prices for individual assets, such as generators and loads, or analyzing the resulting settlement. To address this gap, this paper derives shadow prices from a transient stability-constrained optimal power flow in which the differential-algebraic equations governing the system dynamics are embedded through direct discretization. Each shadow price decomposes into a nodal energy component and a stability component, the latter formed by the dual variables of the dynamic equations in which the power of the asset appears. Shadow prices are therefore asset-specific: co-located generators and loads are assigned the same nodal energy component, but can be subject to different stability components when their transient behaviors differ. Case studies on the WECC 9-bus system and a modified IEEE 39-bus system show that settlement at the proposed shadow prices allocates the cost of the stability constraints among the assets without any side payment. An asset that reduces the stability margin is charged its stability component, whereas an asset that improves it is paid, and no generator is dispatched at a loss.
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Signal Processing
Contactless gait-derived features as candidate digital biomarkers of depression severity in adolescents
Shuai Liu, Xiang Li, Guangxin Dai et al. (+8 more)
Timely assessment of depression severity in adolescents is essential for guiding clinical care, yet conventional evaluations rely on episodic clinical assessments and symptom rating scales. Contactless gait analysis offers a potentially scalable approach to capturing objective behavioral features associated with depression. Here, we introduce the Long-Distance 3D Adolescent Depression Gait
Dataset (LD3D-ADG), comprising depth-sensor gait recordings from 119 adolescents at a single center, including 39 healthy controls, 41 with mild depression, and 39 with moderate-to-severe depression. We developed UMLPA-GCN, a graph convolutional network that models spatiotemporal gait dynamics across three anatomically defined skeletal partitions. In participant-independent five-fold cross-validation, UMLPA-GCN achieved 75.63% classification accuracy, a macro-F1 score of 75.52%, and a macro-area under the receiver operating characteristic curve of 0.839 (95% confidence interval, 0.779–0.899). Class-specific recall was 87.18%, 63.41%, and 76.92% for healthy controls, mild depression, and moderate-to-severe depression, respectively. At the participant level, reduced arm swing, slower pelvic movement, restricted joint range of motion, and greater arm-swing asymmetry were identified as leading potential digital biomarkers contributing to depression severity assessment, associated with higher 17-item Hamilton Depression Rating Scale scores. These findings provide proof-of-concept evidence for contactless digital depression severity stratification in adolescents. Further external and prospective validation is needed to establish their utility for longitudinal monitoring and timely clinical assessment.
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All authors Shuai Liu, Xiang Li, Guangxin Dai, Mengyuan Yu, Wenkong Wang, Hanchi Liu, Jiahui Niu, Yujie Nie, Yumei Wang, Xin Ma, Lizhou Fan
Signal Processing
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults
Youxiang Sun, Yujie Nie, Zhuoru Wu et al. (+6 more)
Objective digital measures of depression severity could complement conventional symptom assessments and facilitate more timely clinical evaluation. Here, we investigated whether task-evoked eye movements provide interpretable behavioral markers of depression severity in adolescents and young adults. In this single-center, cross-sectional study, 155 participants aged 14–20 years completed emotional-spatial free-viewing and
memory-guided recall tasks and were stratified by 17-item Hamilton Depression Rating Scale scores. We examined 19 prespecified eye-movement features across six task-informed domains using
covariate-adjusted ordinal-trend models. Greater depression severity was associated with lower gaze-allocation
entropy (standardized $\beta=-0.599$), fewer inter-area switches ($\beta=-0.526$), longer first target-entry latency ($\beta=0.572$), and lower target dwell proportion ($\beta=-0.678$; all false-discovery-rate-adjusted $q<0.001$). Emotion-specific attentional-bias measures were not associated with severity, whereas associations involving fixation dispersion weakened after adjustment for anxiety symptoms. In a complementary machine-learning analysis incorporating 44 candidate eye-movement features and demographic
covariates, a three-stage cascade classifier achieved the highest four-class stratification performance (accuracy, 0.665; macro-F1, 0.670; area under the receiver operating characteristic curve, 0.869), outperforming single-stage and two-stage approaches. Together, these findings identify altered visual exploration and
memory-guided gaze control as candidate digital biomarkers of depression severity, rather than generalized negative attentional bias. These results support further prospective evaluation of task-evoked eye tracking as a complementary approach for timely severity assessment and clinical decision support.
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All authors Youxiang Sun, Yujie Nie, Zhuoru Wu, Mengyuan Yu, Wenkong Wang, Xiang Li, Yumei Wang, Xin Ma, Lizhou Fan
Image and Video Processing · Cross-listed from cs.CV
Simplifying and Improving On-Policy Distillation for Few-Step Diffusion
Jiawei Zhang, Ziyu Wan, Nanye Ma et al. (+5 more)
Few-step
distillation reduces the sampling cost of
diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures. We show that simple on-policy velocity regression already provides a strong foundation for 4-NFE generation. To represent teacher velocity variation within each coarse transition, we introduce Pivot, a training-only residual proxy that separates local corrections from the student's interval-mean prediction. We further repel the student from weaker teacher predictions, providing a lightweight mechanism for avoiding undesirable modes. The resulting framework, Simplified Matching
Distillation (SMD), remains entirely regression-based, requires neither online score estimation nor
adversarial training, and leaves the deployed generator unchanged. We evaluate SMD on image generation and editing, 3D generation,
video synthesis, and joint audio–
video generation, scaling to 33 billion parameters. At 4-NFE, SMD outperforms strong few-step baselines and surpasses multi-step teachers on several
benchmarks. It also supports cross-model, multi-teacher, and causal-
video distillation.
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All authors Jiawei Zhang, Ziyu Wan, Nanye Ma, Long Zhao, Dong Chen, Hongyu Liu, Qifeng Chen, Dongdong Chen
Audio and Speech Processing
PhonoVoxtLM: Exploring Single-Codebook Phonological Tokens for Speech Understanding and Generation
Mingrui Liang, Kentaro Onda and Shinji Watanabe
We investigate how
speech token representations can support both understanding and generation within a unified
speech language model. We present PhonoVoxtLM, built on the existing VoxtLM framework using phonological
tokens designed to retain linguistic content and prosodic information. The existing tokenizer is jointly trained for
speech recognition and resynthesis. Its single-codebook units serve as both
speech inputs and generation targets, without additional
speech-
token prediction modules. Experiments show improvements over HuBERT-based baselines across understanding and generation under different training-data scales. Further comparisons with WavLM-based
tokens reveal trade-offs between recognition and synthesis, highlighting the importance of evaluating shared representations across both capabilities. We further extend the framework to spoken question answering through fine-tuning with replay. The adapted model improves answering performance over similarly adapted HuBERT baselines while retaining advantages on the original
speech tasks. Our findings support single-codebook representations designed for both content and prosody as a practical choice for unified multitask
speech modeling.
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Signal Processing
A Three-Axis Rydberg Atomic Receiver Model for Polarization-Based Direction-of-Arrival Sensing
Bangyan Xia and Bo Tan
This paper develops a three-axis Rydberg atomic receiver model for direction-of-arrival (DoA) estimation of the
radio frequency (RF) signal source. Three nominally orthogonal RF local oscillators (LOs), each assigned a distinct frequency offset, project the weak incident RF field into resolvable complex beat coefficients in the optical readout. A beat-vector observation is obtained by adopting a response calibration mechanism that maps the coefficients onto the three axes of the Cartesian coordinate system. For a known circularly polarised RF signal, this observation induces a direction-dependent Rydberg steering vector, enabling maximum-likelihood DoA estimation. The pro posed procedure translates the Rydberg beat-vector front end into a highly sensitive RF field DoA sensor. Simulation results show that trace-level beat extraction supports the proposed DoA sensing workflow and provides a basis for comparison with a traditional vector
antenna (VA)
benchmark.
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Systems and Control
Evidence-Governed Deployment Readiness for Networked Agents on Industrial OT Infrastructure
Dibyajyoti Nath, Nitya Sathyavageeswaran, Ziran Min et al. (+1 more)
Deploying a workload on a cyber-secure industrial operational-technology (OT) network requires many cross-layer prerequisites to hold at once, including persistent addressing, name resolution, time synchronization, certificate trust, proxy traversal, and reachable industrial-protocol endpoints. These are owned by different teams, yet current tools treat pre-deployment checking, fault attribution, remediation, and ongoing compliance as separate problems, so evidence is rarely reused across stages. As edge platforms come to host self-adapting
agents on shared infrastructure, this fragmentation becomes a governance problem. All four stages ask one question: do a workload's requirements hold on a given network? We present an evidence-governed framework that answers it through a single reference, a versioned network-intent artifact specifying a workload's cross-layer requirements for a specific release. A coverage-driven test loop attaches tested evidence to each requirement before deployment. At a site, readiness is reported as ready, not ready, or undecidable, separating untested requirements from genuine failures; a repair step proposes fixes that preserve co-located workloads; and a drift-watch component re-evaluates the site on change, extending assurance across the workload's lifetime. A multi-workload OPC UA commissioning case illustrates all four stages. By detecting runtime deviations and suspending an
agent's guarantees until a refreshed artifact is registered, the framework grounds auditable governance for adapting
agent collectives on shared industrial infrastructure.
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All authors Dibyajyoti Nath, Nitya Sathyavageeswaran, Ziran Min, Pavan Athreya Narasimha Murthy
Image and Video Processing
Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling
Anuska Sarkar, Partha Acharya, Vishnu Nandury et al. (+2 more)
We present a real-time, hybrid framework for occlusion handling and depth-aware tracking in minimally invasive surgeries (MIS). Occlusions caused by surgical instruments often obscure the region of interest (ROI), affecting surgical precision. To address this, our approach integrates enhanced You Only Look Once (YOLO) based detection, Particle Filtering (PF), and monocular depth estimation using Multiple Depth Estimation Accuracy with Single Network (MiDaS). Occlusion is detected using a distance-based threshold between instruments and ROI. Experimental results demonstrate robust instrument detection with a confidence score of 0.85, effective occlusion event handling, and accurate depth prediction with an MAE of 0.34 cm and RMSE of 0.53 cm. Independent phantom-based validation against stereo depth measurements yielded an MAE of 0.65 cm and RMSE of 0.66 cm. The framework further achieved an occlusion detection accuracy of 81.95% while maintaining real-time performance at 38.7 FPS. Preliminary surgeon feedback indicates that the proposed visualization framework is clinically relevant and holds promise for future intraoperative image-guided navigation and decision support in MIS.
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All authors Anuska Sarkar, Partha Acharya, Vishnu Nandury, Kaustav Basu, Subhamoy Mandal
Systems and Control
Impact of Security Modeling Fidelity on Hyperscale Data Center Hosting Capacity Assessment
Haoxiang Wan, Linhan Fang, Jonghwan Kwon et al. (+5 more)
This paper assesses hyperscale data center hosting capacity in transmission grids. Hosting capacity (HC) analysis quantifies the maximum additional load a bus can accommodate without violating operational constraints. This paper defines data center HC as the maximum time-invariant active power demand at a single target bus over a 24-hour peak-day horizon, and evaluates it under four models of increasing fidelity (i.e., the extent to which the model enforces real-world operational security requirements) on the modified IEEE 24-bus system: (i) DC optimal power flow (DC-OPF) without unit commitment (UC), (ii) basic SCUC (security-constrained unit commitment) without reliability constraints, (iii) SCUC with spinning reserve, and (iv) N-SCUC with both reserve and branch-contingency security. Comparing these models quantifies the impact of unit commitment, reserve requirements, and N-1 security on bus-level HC estimates. The N-SCUC yields an average HC of 444 MW; omitting N-1 security raises this to 532 MW, a 19.8% overestimation. The DC-OPF and basic SCUC models further overestimate HC, indicating a trade-off between model complexity and estimation accuracy.
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All authors Haoxiang Wan, Linhan Fang, Jonghwan Kwon, Jesus Silva-Rodriguez, Jianyi Yang, Jaya Yellajosula, Fan Jiang, Xingpeng Li
Signal Processing
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification
Mohamed Rabie, Chinthana J. Panagamuwa and Konstantinos G. Kyriakopoulos
Automated radar waveform recognition classifies the waveform and modulation type of a received signal. As new waveform variants appear over time, a deployed classifier must learn them without retraining from scratch. Catastrophic Forgetting occurs when learning new waveforms overwrites prior knowledge. Continual Learning (CL) methods tackle this challenge, but prior radar studies only evaluate a few methods on narrow
benchmarks. Practitioners lack evidence on which method to deploy or which mechanisms are effective. This work presents the first large-scale
benchmark of continual learning for radar waveform recognition. Eighteen CL methods are evaluated on a sequential ten-task problem, drawn from four public
datasets spanning 54 waveforms, under task- and class-incremental (TIL, CIL) settings and single- and multi-epoch regimes. Methods that retain data from old tasks lead both TIL and CIL settings. Plain replay alone closes 77-88% of the gap between the lower and upper practical limits under TIL, but only 46-55% under CIL. The internal mechanisms of the leading methods are ablated and statistically tested to identify effective mechanisms. Each ablation is paired against its full method over n=6 training seeds, topped up to n=12 when inconclusive, and p-values are Holm-Bonferroni corrected within each method's family. Ablation results show that several mechanisms confer smaller but statistically significant gains. Combining these mechanisms improves the leading F1 score by 3.4-5.5% and 2.7-4.9% under TIL and CIL, respectively, and the combined models exceed every benchmarked method in both settings for all SNRs. These results give researchers of radar waveform classification systems a mechanism-level basis for design.
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Systems and Control
Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment
Haoxiang Wan, Xingpeng Li and Jonghwan Kwon
The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions. This paper examines how large data center loads sited at multiple buses affect co-optimized Transmission Expansion Planning (TEP) and Generation Expansion Planning (GEP). The problem is formulated as a multi-year, Unit Commitment (UC)-embedded mixed-integer linear program on the modified IEEE 24-bus Reliability Test System (RTS-24) with Electric Reliability Council of Texas (ERCOT)-calibrated renewable energy profiles. A 500 MW data center load, about 17.5% of the base-year peak, is allocated across four buses by voltage level, generation headroom, renewable co-location, and network connectivity. Integrating this load raises the 15-year net present value of total system cost by 1,794 million USD (+61.6%) and the system-average locational marginal price (LMP) by 45.2%, with operating cost dominating capital investment (84.2% versus 15.8%). Renewable curtailment remains negligible: existing renewables are already near full utilization, so the added demand is served almost entirely by incremental thermal generation. Most notably, the load shifts the optimal plan from transmission-only to generation-only, replacing the base-case 230 kV reinforcement with a new 400 MW combined-cycle gas turbine and showing that strategically sited generation can substitute for network reinforcement.
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Systems and Control
Computation of Price Signals for Controlling Flexible Prosumers in Distribution Grids Incorporating Stochasticity and Fairness
Plouton Grammatikos, Chloé Voirol, Pierre Roduit et al. (+1 more)
This paper proposes a method for distribution system operators (DSOs) to design spatially fair dynamic price signals for flexible prosumers, accounting for both distribution grid constraints and the
stochastic behavior of prosumers. Since locational pricing can introduce price unfairness when grid constraints are active, the proposed approach includes mechanisms to enforce price
fairness across the network. The method involves the DSO solving a scenario-based optimal power flow (OPF) problem to derive, similarly to locational marginal prices in transmission systems, the Lagrange multipliers associated with grid constraints and to compute individualized price signals for each prosumer under various PV generation and demand scenarios. Subsequently, two methods are introduced to aggregate these individualized prices into a single fair price across prosumers. Their performance is evaluated through simulations on a model distribution grid, comparing prosumer revenues, grid constraint violations, and analyzing the impact of
fairness on reducing cost variance among prosumers.
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All authors Plouton Grammatikos, Chloé Voirol, Pierre Roduit, Fabrizio Sossan
Systems and Control
Adaptive Handling of Deadline Misses via Stochastic MPC
Melanie Gallant, Paolo Pazzaglia, Christoph Mark et al. (+2 more)
Real-time control systems may experience sporadic computational overloads that cause controller executions to overrun their expected deadlines, affecting the timely update of control inputs. Such overruns are classically handled with static decisions, e.g., by either stopping and discarding the late execution at the deadline instant, or continuing it while skipping the next activations. This paper first demonstrates that such static policies can lead to suboptimal performance when the controlled system undergoes varying operating conditions, and thus proposes a control-driven hybrid overrun strategy that optimally adapts the deadline miss handling at runtime. We model the closed loop as a Markov jump linear system, whose modes encode overrun histories, and we formulate a scenario-tree-based
stochastic model predictive control problem that predicts and optimizes over possible sequences of deadline hits and misses. We evaluate our proposal on linear
benchmarks including a double integrator and an aircraft pitch system.
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All authors Melanie Gallant, Paolo Pazzaglia, Christoph Mark, Kevin Schmidt, Martina Maggio
Systems and Control
Infrastructure-Aware Runtime Protection of Co-Deployed AI Services at the Industrial Edge
Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min et al. (+1 more)
In modern production lines, a single edge device simultaneously runs multiple AI services like a torque-control model, a
vision inspector, a predictive maintenance routine. All these services run on the same CPU and same shared network segment. When the shared network segment saturates, the services do not crash. The services just produce outputs that are inaccurate or stale, and no longer reflect the plant's conditions. Our paper targets this class of silent, partial failure scenarios in Industrial Edge Systems. We treat each co-deployed service as an autonomous
agent. We introduce the Runtime Criticality Score (RCS), which each service
agent uses to express at any moment, its intrinsic utility and survival urgency under resource constraints. The score is computed from six normalized dimensions covering physical consequence, inference quality, input freshness, recent availability stress, actuator coupling, and available protective actions on the host. Its dimension weights are adjusted online through a lightweight
Bayesian update driven by the type of disturbance currently observed, so the same service is ranked differently under a network delay than under thermal throttling.
Agents publish their RCS to peers over an open, peer-to-peer discovery channel, and use it to negotiate three things without a central controller: (i) local threshold enforcement on their own host, (ii) migration to a willing peer when the current host can no longer protect them, (iii) reassignment of the coordination role when the incumbent coordinator is itself impaired. Beyond the immediate benefit of catching silent degradation, the framework contributes a concrete instance of decentralized, physically grounded
agent coordination.
Agents belonging to different owners can advertise capabilities, bid for protection, and reorganize across an open network.
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All authors Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min, Pavan Athreya Narasimha Murthy
Systems and Control · Cross-listed from cs.CR
BALLAST: Physics-Aware Moving Target Defense for Industrial Control Systems Using a Digital Twin Safety Oracle
Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo et al. (+2 more)
Industrial Control Systems run on static network configurations, which leaves an attacker unlimited time to map the plant. Moving Target Defense answers this by shuffling addresses, but almost every published scheme never checks whether the reconfiguration will itself disturb the physical process it protects, so the defense can destabilise a control loop or trip an actuator. We present BALLAST, which places a learned Digital Twin between the security policy and the plant as a safety oracle. A physics-informed LSTM surrogate forecasts the physical effect of a candidate network change, a reinforcement learning
agent trades security
entropy against process stability, and a connection-draining handover lets in-flight Modbus transactions complete before the old mapping is withdrawn. An explicit shield rejects any action whose predicted trajectory would leave the safe operating envelope. On the SWaT water treatment testbed, Ballast cut the success rate of an adversary's reconnaissance sweep by 85 percent and recorded no safety violation across 50 runs, a 95 percent upper bound of 5.8 percent on the violation rate, against five violations and a tracking error 19 times larger for RL-MTD, the closest published analogue to our design. Measured against the strongest baseline on the physical metrics, a threshold-triggered scheme that itself records one violation rather than RL-MTD's five, Ballast still improves the tracking error by a factor of 2.5 and removes that remaining violation. The gain costs a small and deliberate concession in raw deterrence. Twin inference meets the 50 ms budget on a workstation core and an embedded GPU, and is projected to meet it on both edge CPUs when the three envelope stages are evaluated in a single batched call; the complete decision-to-enforcement path was measured only on the workstation.
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All authors Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo, Andres Caro, Mar Avila
Systems and Control
On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method
Xinda Shen, Yifan Su, Bo Hu et al. (+3 more)
The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution
algorithm.
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All authors Xinda Shen, Yifan Su, Bo Hu, Yanbo Chen, Changzheng Shao, Tao Wu
Signal Processing · Cross-listed from cs.LG
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting
Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet et al. (+2 more)
Advection-
diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn, enhances solar power production forecasts and facilitates the integration of additional photovoltaic (PV) sources into the power grid. However, both classical numerical models and emerging deep learning approaches face challenges when applied to spatially irregular sensor networks such as those formed by weather stations. In this work, we adopt a graph signal processing perspective and model advection-
diffusion particles as signals on a graph to capture the dynamics of these processes. We introduce a Physics-Informed Graph Neural Network (PING) for forecasting future advection-
diffusion particle concentrations on irregular grids. The model incorporates a discretized advection-
diffusion equation as a soft constraint in the loss function to guide learning toward physically consistent solutions. This model is on par with state of the art models on synthetic and real transport phenomena
datasets, including sea surface temperature and cloud index
datasets. Furthermore, the PING model is successfully coupled with models for forecasting PV power production. This hybrid approach leverages data fusion from ground-based sensors at PV stations and satellite images with cloud concentration to improve the accuracy of intra-day PV power production.
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All authors Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet, Pascal Frossard, Rafael E. Carrillo
Systems and Control
Fast Analytical Method for Resource Adequacy Assessment in Renewable-Rich Power Systems
Seyed Amir Hosseini, Mohammad Reza Karimi Gharigh, Antonello Monti et al. (+2 more)
This paper proposes a novel analytical method for resource adequacy assessment in renewable rich power systems. The approach begins by applying the stress strength method to estimate two key resource adequacy indices: loss of load expectation (LOLE) and expected energy not supplied (EENS). This method requires defining the probability distribution functions (PDFs) for both the generation side and the load side. The load is assumed to follow a normal probability distribution, while the generation side probability distribution is derived from the combined PDFs of conventional, wind, and solar power sources. The approach models the cumulative impact of uncertainties on resource adequacy using the central limit
theorem (CLT), and improves accuracy with a cumulant based Gram Charlier expansion when the CLT alone is insufficient. The effectiveness of the proposed method is validated through Monte Carlo simulations conducted on the IEEE single area and multi-area RTS-96 test systems. The results demonstrate that the proposed method achieves a substantial reduction in computational time, which is crucial in the context of operational planning, without sacrificing accuracy. Furthermore, its performance improves as renewable penetration increases, making it particularly suitable for future power systems operation and planning.
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All authors Seyed Amir Hosseini, Mohammad Reza Karimi Gharigh, Antonello Monti, Peter Palensky, Saeed Peyghami
Systems and Control
Data-Driven Surrogate-Model for Real-Time Loads Prediction in Floating Wind Farms
Federico Bellini, Majid Ebrahimi, Alessandro Fontanella et al. (+4 more)
This work presents a data-driven load-surrogate modelling framework for real-time control of floating offshore wind farms. The proposed methodology uses mid-fidelity simulations performed with OpenFAST and FAST.Farm together with MLife fatigue analysis to generate Damage Equivalent Load (DEL)
datasets of floating wind turbines under wake steering conditions. A two-stage surrogate modelling approach is adopted: first, a Feed-Forward Neural Network (FFNN) is trained using data from simulations of a single floating wind turbine, while a second stage corrects the residual error between the FFNN predictions and FAST.Farm simulations using either Gaussian Process Regression (GPR) or an additional FFNN. The results show that the proposed framework can estimate DELs with errors generally below 15% for tower base bending moment, blade root bending moment, yaw bearing moment, and fairlead tension, while maintaining computational times compatible with real-time applications. The study also highlights the challenges related to generalization of the load-surrogate model under varying environmental and control conditions, especially for large floating wind farms. Overall, the proposed methodology demonstrates the potential of surrogate-based DEL estimation for load-aware floating wind farm control applications.
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All authors Federico Bellini, Majid Ebrahimi, Alessandro Fontanella, Marcus Becker, Jan-Willem Van Wingerden, Sara Muggiasca, Marco Belloli
Signal Processing · Cross-listed from cs.CV
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection
Rashi Niyas P, Ritika Jain and Hitika Tiwari
Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Existing methods either rely on hand-crafted features with shallow classifiers, which limits learning from raw signals, or train deep networks on full
datasets that require re-sampling to counter class skew and demand substantial data and compute. To address this, we propose a novel Fall detection framework via Normalized-gradient and
Embedding matched Synthetic Time-frequency condensation, dubbed FALLNEST, a data-centric approach that distills large, imbalanced IMU
datasets into tiny, class-balanced synthetic training sets. Building on dual-domain time-series condensation (CondTSC), FALLNEST introduces a fall-specific 7-channel input with accelerometer vector magnitude, class-balanced K-Means initialization, and normalized gradient matching with gradient clipping, combined with MMD-based
embedding alignment and multi-view
Fourier-domain augmentation, so that class balance arises from the condensed set itself rather than from re-sampling. We evaluate on SisFall, KFall, and MobiFall using strict subject-wise splits and naturally imbalanced test sets, reporting accuracy, Macro-F1, and PR-AUC over five seeds. A 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows (about only 1% of the training windows) reaches 93.5-95.8% mean accuracy and 0.89-0.98 PR-AUC, with standard deviations of 1.0-1.7% in accuracy, demonstrating the efficacy of our approach.
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Systems and Control
Generic Local Identifiability Does Not Imply Generic Global Identifiability in Linear Networks
Anders Hansson
We give a counterexample to the implication from generic local to generic global identifiability for linear networks with a fixed
topology, independently unknown edge coefficients, and selected excited and measured nodes. A six-node network with nine unknown coefficients has exactly two compatible parameter vectors generically. Adding six excited and measured nodes preserves this ambiguity while making the added coefficients directly recoverable. The resulting twelve-node structure has forty-two independent unknown coefficients and zero diagonal. For every positive bound on the largest
eigenvalue magnitude, a nonempty open set of parameters has two distinct compatible networks satisfying that bound. This holds for both real and complex coefficients. Other open regions have exactly one admissible solution. The proof uses a rational transformation exchanging the two solutions and a common limiting
matrix whose fourth power vanishes. Exact rational-number certificates verify an explicit pair. The construction also extends algebraically when the unknown coordinates are whole rational transfer functions. Genericity in prescribed numerator and denominator coefficient families, and the requirement that every transfer function vanish at infinite frequency, remain separate questions.
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Signal Processing
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction
Shiliang Lu, Lu Bai, Ziwei Huang et al. (+1 more)
For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpoints. We propose cross-image routing
attention (CIRA) for sparse association and mutual enhancement of propagation-relevant features from dual-endpoint RGB images. Building on CIRA, CiFormer predicts millimeter-wave (mmWave) beam-pair power
matrices for vehicle-to-infrastructure (V2I) communications. Experiments show that CiFormer reduces mean absolute error and improves top-1 beam-pair selection accuracy by 3.15 percentage points over the strongest baseline. Visualizations and ablation studies support dual-endpoint sensing and selective cross-view interaction in capturing propagation-relevant dependencies, suggesting broader applications in wireless tasks beyond beam prediction.
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All authors Shiliang Lu, Lu Bai, Ziwei Huang, Xiang Cheng
Signal Processing · Cross-listed from cs.LG
Revisiting Error Feedback for Model Quantization
Wai-Yiu Keung, Chi Cui, Wing-Kin Ma et al. (+2 more)
We study causal error-feedback coefficients as design variables for model quantization. Modeling instantaneous quantization errors as independent, zero-mean variables with a common fixed variance, we formulated a metric-dependent trace problem for designing feedback coefficients. The formulation resembles fixed-order GPTQ/LDLQ feedback and supports Jacobian-aware designs that account for downstream error propagation. For a positive-definite metric $\boldsymbol{H}$, we prove that the joint rotation-feedback trace minimum is $n\det(\boldsymbol{H})^{1/n}$ and present an attaining pair using the
geometric mean decomposition. Experiments on synthetic networks, trained MLPs, and
language models show benefits from downstream-sensitive feedback, but also reveal that trace optimality need not improve finite-level performance. Small QAT experiments further illustrate direct loss-driven feedback tuning with fixed or trainable network weights.
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All authors Wai-Yiu Keung, Chi Cui, Wing-Kin Ma, Jesús Cid-Sueiro, Hei Victor Cheng
Systems and Control
Preliminary Exploration of Large Models Based on Self-Supervised Learning for Health Monitoring of Power Electronic Converters
Xinyuan Liao, Xiangtian Deng, Yang Liu et al. (+3 more)
Current artificial intelligence (AI) methods for power electronics converters make limited use of the abundant unlabeled data generated during real-world operation. This underexplored resource holds significant potential for advancing model performance. To address this gap, this paper presents PE-TSFM, a time-series foundation model tailored for power module health monitoring. The model adopts the PatchTST architecture combined with self-supervised learning and is trained on more than 143 million timestamps, where over 98% of this training data is unlabeled and collected directly from an operating traction converter. In degradation classification tasks, PE-TSFM achieves an accuracy of 87%, considerably surpassing five conventional time-series AI models whose performance ranges from 27% to 45%. These results demonstrate the model's strong few-shot learning capability and generalization across different operating conditions. This work promotes a shift from narrowly focused models in power electronics toward unified, scalable foundation models that can unlock value from large-scale unlabeled operational data.
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All authors Xinyuan Liao, Xiangtian Deng, Yang Liu, Zhaobing Cai, Haoran Wang, Yi Zhang
Audio and Speech Processing
Self-Supervised Source-Filter Model of Speech Production
Nani Meka, Raghavan Ramesh and Sri Rama Murty Kodukula
Deep learning models of
speech seldom represent the source-filter process by which
speech is produced, relying instead on latent features with no physical correspondence to articulation or phonation. We propose SoFilNet, a self-supervised, differentiable Source-Filter Network that estimates the parameters of
speech production directly from the raw waveform: a source, specified by fundamental frequency (F0), voicing and the aperiodicity of five frequency bands, and a filter, specified by ordered formant poles, additional poles that shape the rest of the spectral envelope, and antiresonance zeros. The model is trained only to reconstruct the input
speech; no formant or pitch annotation is used. Since per-bin spectral losses favor blurred spectra wherever the model is uncertain, we train F0 and voicing against autocorrelation pseudo-labels refined with the model's own filter, aperiodicity against the periodic fraction of each band, and formant sharpness against spectral contrast. On VTR-FORMANTS, the estimated formants attain the lowest mean error among the reported methods, including supervised trackers, and on the unseen PTDB-TUG corpus the F0 frame error matches the best classical tracker and is lower than that of all learned trackers. The parameters are also consistent with
speech production theory: aperiodicity rises with frequency and orders phone classes by the
turbulence of their excitation, and the zeros deepen on nasal consonants, the lower one rising with place of articulation.
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Signal Processing · Cross-listed from math.OC
Lifted Restricted Isometry Property and Fast Algorithm for Spectral Compressed Sensing With Near-Optimal Sample Complexity
Xunmeng Wu, Jian-Feng Cai, Zongben Xu et al. (+1 more)
This paper studies the spectral compressed sensing problem, which aims to recover an $r$-spectrally-sparse signal of length $N$ from linear compressive measurements. We reformulate the problem as the recovery of a low-rank Hankel
matrix. Motivated by this formulation, we propose a Lifted Restricted Isometry Property (LRIP) for the measurement
matrix and show that Gaussian measurement
matrices satisfy LRIP under certain conditions. Unlike classical RIP, LRIP imposes a rank constraint in the lifted
matrix domain while taking measurements in the signal domain through the adjoint Hankel operator. To solve the resulting nonconvex problem efficiently, we introduce a Fast Iterative Hard Thresholding (FIHT)
algorithm. Based on LRIP, we show that FIHT converges linearly to the true signal with high probability using $\mathcal{O}(r \ln r \ln^2 N)$ scaled Gaussian measurements, which is near-optimal up to log factors and improves the state-of-the-art order of $r^2$ for nonconvex Hankel methods. Extensive numerical results demonstrate the superiority of FIHT as compared to state-of-the-art convex methods in terms of phase transitions, computational time, and robustness to noise.
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All authors Xunmeng Wu, Jian-Feng Cai, Zongben Xu, Zai Yang
Systems and Control · Cross-listed from math.OC
Control Realization of Schrödinger Bridges over Discrete-Time Nonlinear Systems via Maximum-Entropy Distribution Steering
Kaito Ito and Kenji Kashima
This paper shows how a Schrodinger bridge (SB) structure emerges from maximum-
entropy (MaxEnt) distribution steering of discrete-time nonlinear systems. We also establish that the resulting bridge can be realized by an optimal control policy. We consider general state and input costs, non-Gaussian endpoint distributions, and input constraints. Assuming the existence of a feasible policy with finite objective value, we show that the state-path measure under a MaxEnt optimal control policy is the unique SB. The reference measure of this SB is induced by an optimal policy of the corresponding terminal-unconstrained problem. Moreover, we give an equivalent characterization of the optimal state-path measure using a killed reference and its normalized Feynman–Kac measure, thereby interpreting the state cost as killing. We then characterize the finite-cost feasibility by the existence of an endpoint coupling having finite Kullback–Leibler divergence from the reference endpoint distribution. For compactly supported marginals with bounded target density, we derive reachability-based sufficient conditions guaranteeing this feasibility. We further provide specializations to fully actuated control-affine and reachable linear systems.
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