Electrical Engineering and Systems Science

This paper proposes a novel methodology for human-aware and fair service restoration in power distribution networks, explicitly accounting for the customer experience of outage duration. The complexity of this problem stems from the inherently unpredictable and stochastic nature of power outage events. Traditional approaches often oversimplify the problem by treating failures as deterministic, overlooking the lived experiences of customers and the true uncertainty of outage patterns. In contrast, the proposed method incorporates the probability of potential failures to guide a customer-aware and fairness-driven resource allocation, ensuring that restoration is not only fast but also perceived as fair from the customer's perspective. To achieve this, a spatially distributed, adaptive, and scalable partitioning policy is designed to balance restoration time across all failure locations, promoting consistency and equity in the outage experience. Next, an adaptive and distributed repair crew dispatch algorithm is proposed to accelerate service restoration while ensuring that no customer segment is disproportionately affected. The framework leverages a Receding Horizon (RH) optimization algorithm to dynamically minimize total restoration time amid randomly occurring outages in both space and time. Simulation results on modified 69-bus distribution networks under stochastic outage conditions demonstrate the model's effectiveness in delivering socially fair and customer-sensitive restoration outcomes

The Frechet Inception Distance (FID) compares two image sets by fitting a Gaussian to the features of a fixed network and measuring the distance between the two Gaussians. In digital pathology the Inception network is routinely replaced by a histology foundation model, on the assumption that a domain encoder gives a more meaningful score. We show that this choice changes the result. For one fixed pair of tile sets, the raw Frechet distance varies about thirty-fold across six common encoders, and the ordering does not follow embedding dimension, so a raw score cannot be read without naming the encoder. Using a held-out in-house cohort (about 500,000 H&E and immunohistochemistry tiles from 2,119 slides) and a public TCGA BRCA cohort (100 slides), we benchmark Inception-v3, Phikon-v2, CONCH, UNI2-h, Virchow2 and Prov-GigaPath across within-cohort baselines, cross-cohort drift, controlled perturbations, compression, stain normalization, and two generative models. Expressing each distance as a ratio to the encoder's own within-cohort floor restores comparability, cutting the across-encoder coefficient of variation by about 89% within cohort and 58% across cohorts. The encoders separate into a sensitive group (CONCH, Phikon-v2, Inception-v3) and an invariant group (UNI2-h, Virchow2, Prov-GigaPath), and this split decides which generative model is judged more realistic, so the encoder can change the conclusion of a generative evaluation. At the slide level, an attention-pooling encoder registers per-slide composition that a pooled patch distance cannot see, raising the distance about 320-fold on matched cohorts. Using the same protocol we evaluate TuroCompress, a proprietary pathology codec, which reaches the highest reconstruction fidelity at the smallest file size among codecs tested. We release the normalization protocol, the per-encoder perturbation panel, and the feature extracts.

Medical image super-resolution (MedSR) requires recovering fine anatomical structures from degraded observations while avoiding unsupported details introduced by generative priors. Large-scale pre-trained multimodal diffusion transformers provide strong visual priors, but their adaptation to MedSR remains non-trivial. In conventional ControlNet-style adaptation, the low-resolution (LR) image is processed as an external condition and injected into the denoising stream through one-way connections. Consequently, LR anatomical evidence cannot be jointly updated with the evolving denoising and semantic representations. We propose MedDiT4SR, a tri-stream adaptation framework that integrates the LR, noisy latent, and text representations into the same multimodal diffusion-transformer blocks. To complement global token interaction, we introduce a Super-Resolution Adapter (SR Adapter) that aggregates scale-dependent local tokens and suppresses interpolation-induced redundancy. We further propose a Semantic Alignment Refiner (SA Refiner) that calibrates local LR responses using prompt-conditioned semantic information. Experiments under both in-domain and within-modality cross-dataset settings demonstrate the effectiveness of adapting large-scale pre-trained DiT models to medical image super-resolution across diverse imaging domains.

Machine-learning-based channel predictors must operate under stringent latency, memory, and computational constraints while remaining robust to noisy and time-varying observations. This paper develops a causal channel-prediction framework based on three single-layer gated recurrent unit variants: an unconstrained lightweight GRU (L-GRU), a stability-aware GRU (SA-GRU) with a spectral bound on the candidate-state recurrent matrix, and a doubly constrained lightweight GRU (DCL-GRU) with additional control of the reset-gate recurrent matrix. A sufficient condition is derived for contraction of the complete candidate-state mapping, while preserving the parameter count and inference-time structure of the baseline architecture. These guarantees apply to the candidate-state mapping and do not directly imply contraction of the complete GRU hidden-state transition. The models are trained on 2x2 MIMO channels generated using the 3GPP CDL-A model, and their hyperparameters are selected through Bayesian optimisation with Optuna's Tree-structured Parzen Estimator. Across the considered SNR range, the constrained variants retain competitive prediction accuracy and achieve optimisation runtimes close to L-GRU, with speedups of 1.72x and 1.76x relative to a five-layer GRU for SA-GRU and DCL-GRU, respectively. All audited constrained runs satisfy the prescribed spectral bounds. Under temporary observation corruption followed by recursive prediction, SA-GRU reduces the mean and peak hidden-state deviations by approximately 15.3% and 13.0%, respectively, relative to L-GRU, whereas L-GRU achieves the lowest rollout NMSE. These results highlight an explicit trade-off between prediction accuracy, empirical rollout robustness, and candidate-state stability guarantees.

Near-fi eld sampling seeks to represent electromag- netic fi elds between transmitting and receiving regions using a minimal number of measurement points while preserving the dominant spatial modes. This paper develops a geometry- aware sampling framework based on spatial degrees of freedom (DoF). A view-length formulation is used to derive closed-form expressions for the propagating-mode DoF density for simple line-source geometries, providing both the total DoF and its local distribution. One-DoF sampling points are obtained from equal increments of the cumulative DoF density, yielding an adaptive nonuniform sampling strategy up to the knee of the singular-value spectrum. To improve the representation of the remaining modes beyond the knee, a reactive-mode density is introduced to guide the placement of additional edge samples. An operator-based sampling error functional is formulated and shown to be lower-bounded by the neglected singular values of the continuous channel operator. Numerical results demonstrate that the proposed sampling strategy closely approaches the optimal performance obtained from singular-value decomposition and signifi cantly outperforms sampling based solely on the propagating-mode DoF density.

In this study, a novel encoder-only Transformer-based receiver architecture is presented for BPSK signals transmitted over Faster-than-Nyquist (FTN) signaling channels that introduce intentional inter-symbol interference (ISI) with a compression factor of $\tau=0.8$. A complete end-to-end communication chain encompassing BPSK modulation, RRC pulse shaping, and the ISI coefficients arising from matched filtering was constructed and evaluated. The proposed Transformer receiver was benchmarked against the optimal BCJR detector over an $E_b/N_0$ range of 0-8 dB. To systematically close the BER gap to the BCJR, a two-stage training strategy combining multi-SNR pretraining and per-SNR curriculum fine-tuning was developed. The computational complexity and inference latency of the Transformer receiver were analyzed in comparison with a GRU based receiver. Attention map visualizations revealed that the Transformer autonomously identifies the FTN-induced ISI memory structure without requiring any prior channel knowledge; as the SNR increases, the attention weights become significantly concentrated around the center token and its nearest neighbors.

We propose a notion of robust adaptive backup control barrier functions for nonlinear control affine systems with parametric uncertainty in both the drift dynamics and actuation matrix. Backup control barrier functions guarantee safety by predicting the system's trajectory under a pre-certified safe controller. However, these predictions rely on the model and can be inaccurate when the system contains unknown parameters. To address this issue, we estimate the unknown parameters using element-wise certified adaptive estimators that provide a parameter adaptation law and component-wise estimation error bounds. We compute the backup flow using the estimated model and tighten the safety conditions using these certified bounds. The resulting safety conditions account for the sensitivity of the predicted flow to parameter estimation errors. Moreover, to handle uncertainty in the actuation matrix, we use a duality-based reformulation that enables the use of a computationally efficient quadratic-program-based safety filter. We prove that controllers satisfying the proposed robust adaptive backup control barrier function constraints guarantee safety under parametric uncertainty and input constraints.

Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks. Traditional approaches, including interpolation and deep learning, either struggle to capture complex propagation effects or require large-scale retraining for each new sampling pattern, which limits their generalization. More recently, prior-based methods have combined pre-trained generative models with measurements to reduce the need for deployment-time model fine-tuning, but they typically treat the prior as a simple regularizer and lack explicit transmitter-aware integration. In this paper, we propose RadioTrace, a novel RM estimation framework without deployment-time fine-tuning that tightly integrates sparse RSS measurements with a frozen pre-trained diffusion prior. RadioTrace incorporates transmitter (Tx) location estimation directly into the denoising loop, iteratively refining Tx coordinates based on reconstruction quality to guide the generative process. To further enhance robustness, we introduce a propagation-guided K-means initialization that mitigates poor local minima in the Tx update and provides a geometry-consistent starting point. Moreover, we provide a stochastic stability analysis for the Tx-coordinate refinement component, showing that the Tx update remains stable under perturbations induced by diffusion sampling and Tx-map relaxation. Extensive experiments demonstrate that RadioTrace achieves competitive performance with state-of-the-art learning-based methods under random sampling, and maintains strong reconstruction quality under restricted-area sampling, highlighting its adaptability, robustness, and practical relevance.

Parkinson's disease (PD) manifests in motor impairments that are detectable through digitized spiral drawings. This study introduces an explainable framework for PD screening using a novel radial-sampling feature fusion approach. We transform 2D spiral images into 1D revolution signals via a systematic ray-sampling technique to extract three distinct revolutions. We integrate spatial metrics, such as inter-revolution spacing variability and RMS radial derivatives, with spectral descriptors derived from Fast Fourier Transform (FFT) analysis across low, mid, and high harmonic bands. A total of 20 features were utilized to train state-of-the-art machine learning models, including Support Vector Machines (SVMs), Random Forests (RFs), and Light Gradient Boosting Machines (LightGBMs). Among these, the RF classifier demonstrated superior performance. Subsequent 5-fold cross-validation stability analysis along with feature importance analysis identified RMS radial derivative of the outer revolution as the most critical biomarker. Stratified Cross-Validation demonstrates that combining spatial and frequency features significantly enhances detection accuracy compared to single-domain methods, facilitating effective clinical deployment even in data-scarce environments. This interpretable pipeline provides a robust, low-cost white-box screening tool, offering a practical alternative to opaque deep-learning models for early clinical intervention.

Safe steerable catheter control is fundamentally a problem of interaction dynamics: the tip must follow a planned motion, remain compliant against moving tissue, reject friction and hysteresis, and respect a clinically meaningful never-exceed contact-force bound. We formulate catheter--tissue interaction dynamics in the scalar tip-normal coordinate of a single-segment single-tendon catheter. A partial-physics feedforward cancels only the reliable nominal bending dynamics, exposing a configuration-invariant linear interaction-dynamics model whose input gain varies through the scalar catheter inertia. A predictive optimizer then regulates this interaction state subject to hard contact-force, tendon-force, and curvature constraints. An augmented Kalman filter compresses contact, friction, and modeling error into one sensor-free disturbance state, giving nominal offset-free regulation in free space while leaving force safety to the explicit constraint. The unconstrained and disturbance-free limit recovers classical catheter impedance as a special realization of the same interaction dynamics, rather than as the main design object. In a MuJoCo distributed-compliance simulation of an eight-link tendon-driven catheter, disturbance augmentation cuts free-space approach error by 90\%, and only the force-constrained predictive interaction-dynamics controller reconciles tracking with the 0.5\,N bound: the unconstrained controller drives contact force to 0.60\,N against a penetrating target, while the constrained one holds 0.47\,N at identical tracking. These results show that offset-free motion regulation and contact-force safety are coupled interaction-dynamics objectives, and that the explicit predictive constraint resolves their tension under stiff tissue contact. The bound also holds under $0.5$\,mm, $1.2$\,Hz cardiac motion. Hardware validation is future work.

Advances in AI-based voice conversion have enabled a wide range of media applications, including films, audiobooks, and games. However, most research and public benchmarks still focus on natural human speech, leaving designed vocalizations, such as monster growls and robotic voices, underexplored, partly due to the lack of publicly available resources. To address this gap, we introduce the Designed Vocalizations Dataset, constructed by curating diverse raw vocal sources, including speech and animal vocalizations, and applying professional vocal effects processing to produce corresponding effect modified variants. We further provide a standardized test set with explicit seen/unseen splits over source timbre groups and preset styles to assess generalization under controlled conditions. Finally, we report baseline benchmark results to support reproducible evaluation and future research. The dataset and demo samples are available at this https URL.

As Integrated Sensing And Communication (ISAC) systems push toward higher sensing resolution, multi-band processing has emerged as a key enabler, with Frequency Range 3 (FR3) (7-24 GHz) standing out for its combination of wide bandwidth and favorable propagation. A common assumption underlying existing multi-band ISAC techniques is that targets behave as frequency-invariant point scatterers, enabling coherent combination of measurements across widely spaced subbands. However, this assumption does not hold over wide fractional bandwidths, since real objects exhibit frequency-dependent scattering mechanisms and migrating scattering centers. This paper provides the first systematic experimental characterization of the frequency anisotropy of everyday objects for ISAC, using channel measurements collected with a calibrated vector network analyzer over the 6-24 GHz band, across 10 objects and 120 viewpoints. We process bistatic channel impulse responses and synthetic aperture radar images to quantify multi-band coherence, extracting the real part of the cross-band correlation coefficient. Our results reveal complex, non-trivial coherence structures that vary substantially with object type and viewing angle, highlighting the need to account for frequency anisotropy in multi-band ISAC system design.

In this paper, we address supervisory control in discrete-event systems with event forcing. In particular, partial observation of the supervisor is considered. Unlike traditional supervisory control, which relies on event enablement and disablement, the forcing mechanism allows a supervisor to preempt unwanted transitions by actively triggering forcible events. Under partial observation, we face a new challenge where the supervisor's forcing decisions must be consistent across all indistinguishable strings meanwhile guarantee all violating transitions are excluded through preemption. We introduce the concept of forcing consistency as a necessary and sufficient condition for the existence of a supervisor that achieves a given specification. We prove that forcing consistency is strictly stronger than the notion of forcibility but, unlike the latter, is not closed under union. These results lay the theoretical foundation for supervisor synthesis in the presence of both forcing mechanisms and observation limitations.

Probabilistic Ising machines (PIMs) based on probabilistic bits offer a hardware-friendly route to solve combinatorial optimization problems, but most digital implementations achieve high throughput by exploiting sparse interactions. This limits their applicability to dense problems, for which memory bandwidth and data movement become the dominant bottlenecks. Here, we show a resource-efficient pipelined Field-Programmable Gate Array architecture enabling high-throughput execution of fully-connected PIMs while maintaining scalability and modularity. This architecture design combines a deeply pipelined (>20 stages) probabilistic bit update path, which overlaps spin evaluation and local-field updates, with a bandwidth-aware on-chip memory organization for the coupling and bias matrices. The architecture supports 512 p-bits with 16-bit fixed-point coefficients and 1024 and 2048 p-bits with 10-bit and 2-bit coefficients, respectively, and operates at up to 300 MHz. At fixed degree of parallelization, it delivers an order-of-magnitude higher update rate than an optimized non-pipelined baseline, while improving the time-area trade-off for dense workloads. Validation on portfolio optimization and low-density parity-check decoding shows close agreement with software references and substantial reductions in time-to-solution relative to the non-pipelined design, establishing pipelining as an effective route to scalable digital probabilistic computing for dense optimization problems.

We present CS-P2L, a framework coupling covariance steering (CS) with the Pick-to-Learn (P2L) meta-algorithm for certified controller synthesis over high-fidelity stochastic simulators. The method iteratively evaluates policies on simulator rollouts, tightens surrogate constraints using the worst-case violations, and provides compression-based probabilistic guarantees on the true violation probability given a confidence level. On a spacecraft powered-descent problem with uncertain gravity, CS-P2L certifies a violation bound of 4.9\% with 600 rollouts, whereas standalone covariance steering underestimates the violation rate by roughly a factor of two.

In [1], the authors recently proposed and solved a surveillance-evasion differential game in which an agile pursuer (the prying pedestrian) seeks to remain within a given surveillance range of a less agile evader for as long as possible while the evader seeks to escape as quickly as possible. In this paper, we provide initial results that extend this game from the 1 versus 1 (1v1) setting to a 2 versus 1 (2v1) setting with two pursuers and one evader. By deriving and exploiting semi-explicit or geometric reinterpretations of the existing 1v1 results, we derive partial solutions to the 2v1 game for the case of static pursuers and for the case of an evader that is at least twice as fast as the pursuers. While the 2v1 results of this paper build on the 1v1 results of [1], a different solution approach is developed to avoid a coordinate transformation that reduces the 1v1 game to two dimensions but which is ineffective at simplifying the 2v1 game. Beyond enabling progress on the 2v1 game, our new approach yields new geometric interpretations of the optimal pursuer and evader strategies in the 1v1 game, and opens further possible extensions.

Objective: Brain-Computer Interfaces (BCIs) enable the control of external devices by decoding user intentions from electroencephalography (EEG). However, substantial EEG variability within and between users remains a major challenge. To better understand this variability, we propose interpretable metrics that independently quantify temporal, spatial, and frequency variability in BCI related brain activity within and between users. Methods: We propose a framework to quantify variability by extracting EEG features and defining variability as their dispersion around their centroid using appropriate distance functions. Using two motor imagery BCI datasets (N = 133 users), we investigated the relationship between BCI performance and the variability metrics through within-user and cross-user classification experiments. Results: Negative correlations of -0.2 to -0.4 were observed across most conditions, suggesting that lower variability is associated with higher BCI performance. Moreover, the metrics revealed differences in robustness to variability between the deep learning and Riemannian-based classifiers, with the former showing weaker correlations. Conclusion: The results demonstrate the effectiveness of the proposed variability metrics and suggest that reducing variability may improve BCI performance while revealing differences in the sensitivity of classification models to different types of variability. Significance: The framework quantifies temporal, spatial, and frequency variability at multiple hierarchical levels (within-trial, between-trial, and between-trial-group), providing interpretable measures to better understand EEG variability and support more robust BCIs. It could also be used to characterize dataset variability, evaluate classifier sensitivity, incorporate variability into objective functions, and provide variability-based user feedback.

In this paper, we revisit the problem of an encrypted model predictive control (MPC) design, representing a significant challenge in the recent field of secure process control. Existing methods in secure optimization-based control are non-existent and even partial implementation fails to address the closed-loop system stability and recursive feasibility properties of the constrained MPC. To overcome these limitations, we propose a novel approach that utilizes a polynomial approximation of the optimal control law. This method evaluates the explicit control law within a fully homomorphic encryption framework, ensuring that the controller is securely deployed on any third-party or cloud-based platform, with both process data and controller coefficients protected. Experimental results from a laboratory-scale implementation and validation of the proposed privacy-aware control method demonstrate its advantages.

Bilateral teleoperation systems that include joint flexibility better reflect real robotic systems used in surgery, space, and rehabilitation. However, joint flexibility together with time-varying communication delays makes it difficult to maintain stable and coordinated motion between the master and slave robots. To address this, we propose a hybrid control method that combines a stable Proportional-plus-Damping (P+d) controller with a model-free deep reinforcement learning agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The P+d controller provides basic stability under bounded delays, while the learning agent adjusts and tunes the remote-side proportional and damping gains in real time to reduce vibrations and improve tracking. Stability is guaranteed for bounded time-varying delays using Lyapunov-Krasovskii analysis. The approach provides a practical solution for teleoperation systems facing both joint flexibility and uncertain network delays.

Reconfigurable intelligent surfaces (RISs) need measurement methodologies that quantify the three-dimensional scattering response of a programmed surface, not only the received power of one bistatic link. This paper presents an automated anechoic-chamber methodology for full-sphere characterization of a varactor-based RIS around 5 GHz. The developed platform combines a state-of-the-art spherical near-field scanner with a near-field-to-far-field transformation, which is suitable for antenna measurements, with a common, low-scattering mount for the RIS and the excitation antenna. It enables programming of several independent DC voltages for remote RIS beam steering configuration via a control unit integrated into the chamber, which features a multichannel digital-to-analog converter. The RIS states are synthesized from a pre-measured phase-voltage characteristic and evaluated against an unbiased-RIS reference using local angular power averages. Measurements at 5.3 GHz serve as a proof of operation: the workflow programs one-dimensional and two-dimensional anomalous-reflection states, localizes the resulting beams over the sphere, and extracts target-window gain, maximum-window gain, and pointing error from the same dataset. For five measured RIS beam-steering states, the prescribed target windows show relative beam-steering gains up to 8.8 dB, while the locally detected main-lobe windows show relative gains up to 14.0 dB with an average of 11.3 dB. The reported gain is not active amplification by the RIS; it is a reference-normalized local power ratio for a fixed measurement configuration.

Understanding how to collect data that is meaningful for control purposes is of paramount importance in data-driven control. While existing approaches have primarily relied on the satisfaction of a rank condition to assess the quality of an experiment, we show that satisfying it is not always sufficient to achieve satisfactory closed-loop performance. Focusing on scenarios where white-noise-like excitation cannot be used for data collection, we examine the frequency-domain implications of linear behavioral representation. This analysis demonstrates that data must both satisfy the rank condition and excite the frequencies of interest for the control goal, thereby laying the foundations for control-oriented experiment design tailored to direct data-driven approaches. These findings are reflected in our numerical results. Data-enabled predictive controllers that rely on data satisfying the rank condition but neglect the tracking control goal result in a closed-loop system that cannot track the selected reference.

Protecting speech privacy in real-life audio recordings is a growing concern. This contribution evaluates the effectiveness of three obfuscation techniques in protecting linguistic speech content, using digit recognition as a task-specific and practically motivated evaluation scenario. As a first baseline, a general-purpose speech recognition model and a digit-specific classifier were applied as informed attackers to recognise both single digits and concatenated digit sequences. Our experimental results demonstrate significant differences in recognition performance across digit modality, speech rate, and attack model. These findings emphasize the need for more comprehensive and application-oriented evaluation methods to ensure speech privacy.

Future integrated sensing and communication (ISAC) networks are expected to operate in dense multi-cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay--Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allocation, receive filtering, and interference. This paper develops a modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems. Starting from a discrete-time OFDM sensing model, we derive closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF). The analysis reveals distinct interference structures: MF depends on fourth-order constellation moments and power-overlap terms, whereas RF is governed by inverse-symbol-power and ratio-type interference terms. Based on these expressions, we obtain sensing-oriented power allocation structures, including a ramped water-filling solution for MF and a square-root allocation rule for RF. Furthermore, we jointly optimize the finite-alphabet constellation selection and power allocation under realistic communication and power constraints, and obtain tractable mixed-integer convex formulations for both MF and RF. Additionally, we study spectrum-overlap coordination in multi-cell scenarios and reveal the distinct MF/RF preferences for shared and orthogonalized tones. Furthermore, we extend the interference model to inter-cell propagation delays exceeding the cyclic prefix (CP), and show how the resultant delay violation redistributes the nominal interference spectrum into a delay-distorted effective spectrum...

Accurate modeling and simulation are essential for the effective design, testing, and evaluation of electric machine systems. However, existing models often face interoperability challenges due to differences in programming languages (e.g., C, MATLAB, Python) and the separation of components such as inverters and controllers across diverse environments. These challenges are amplified by the growing use of advanced simulation platforms like Hardware-in-the-Loop (HIL) and Controller-HIL, which require repeated adaptations for compatibility. This paper presents a HELICS-based co-simulation framework that enables seamless coordination among heterogeneous tools and models, providing a unified platform for integrating and testing electric drive models regardless of their origin. The approach is demonstrated through the co-simulation of an inverter-fed permanent magnet synchronous machine (PMSM) drive under speed control, showcasing reduced development time, flexible reuse of existing models, and efficient integration into both software and HIL environments offering a scalable, modular solution for collaborative and repeatable electric drive system testing and development.

Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving patient outcomes. Speech-based CI detection has emerged as a promising non-invasive approach, as speech signals encode both linguistic and acoustic markers associated with cognitive decline. Recent advances in large language models (LLMs) further strengthen the potential of speech-based assessment by enabling more expressive representation learning and improved generalization across diverse speakers, recording devices, and clinical environments. Moreover, multimodal learning by jointly modeling linguistic and acoustic features allows for a more comprehensive characterization of cognitive and behavioral changes related to CI, leading to more reliable detection. In this work, we propose a multimodal CI detection framework based on open-source LLMs that integrates speech audio and corresponding transcripts while preserving patient privacy. Acoustic embeddings are extracted directly from speech signals, while textual embeddings are generated from automatically transcribed speech. These modality-specific embeddings are then concatenated to create a combined feature vector and used for downstream classification, without requiring access to raw or sensitive patient data. The proposed approach is evaluated on the ADReSS20 and ADReSSo21 benchmark datasets. Experimental results show that the proposed multimodal framework achieves an CI classification accuracy of 92.4% and consistently outperforms single-modality baselines. Our work establishes a new state-of-the-art for CI identification, with the proposed method demonstrating superior cross-dataset generalization. This advance highlights the power of an LLM-based multimodal framework that fuses linguistic and acoustic data to enable robust, scalable, and non-invasive screening.

In conversations, human emotions are transient; however, they tend to persist across multiple utterances. For example, we rarely switch instantly between contrasting emotions such as happiness and anger. Instead, emotions tend to evolve smoothly, and these patterns are often speaker-specific. Some people might escalate, while others gradually cool down over time. Furthermore, when emotions change during a conversation, they are often driven by contextual factors, such as newly received information or unexpected events. Even though progress has been made in Emotion Recognition in Conversations (ERC), most existing approaches still rely heavily on overt evidence and do not sufficiently model these non-apparent factors. Especially in multimodal settings, this makes these models fragile when the signals are noisy (e.g., occluded faces, slang expressions, or microphone noise). To address these limitations, we introduce Speaker-Conditioned Priors over Emotions (SCoPE). SCoPE is a light weight module that utilizes the emotional history of each speaker and explicitly models their priors for use in subsequent emotion classification. Second, we incorporate emotion shift prediction, a well-established concept in ERC, to guide the model in balancing the priors from SCoPE and multimodal evidence. Finally, we propose a shift-aware fusion mechanism that performs precision-weighted logit integration between multimodal evidence and the speaker prior, forming a Bayesian-inspired product-of-experts formulation. This dynamic fusion allows the model to rely on historical priors when emotions persist and to prioritize multimodal evidence when shifts are likely. Experimental results show our model achieves superior performance over recent state-of-the-art models on the IEMOCAP dataset in multimodal settings.

Fine-grained recognition of aquatic species is challenging due to subtle morphological differences and long-tailed distributions, where ultra-rare species are underrepresented. A natural solution is to jointly model segmentation, morphological traits, and species classification within a multi-task learning (MTL) framework. However, existing MTL methods suffer from negative transfer caused by gradient conflicts between low-level dense tasks and high-level classification objectives, degrading fine-grained representations. To address this limitation, we identify gradient interference across hierarchical tasks as a fundamental bottleneck and propose FISHER, a gradient-decoupled hierarchical multi-task learning framework. FISHER aligns optimization with the biological hierarchy of aquatic species by enforcing a unidirectional information flow from segmentation to trait prediction and finally to species classification, while explicitly decoupling gradients across task boundaries. This design prevents high-level objectives from corrupting low-level morphological representations, effectively mitigating negative transfer while preserving the benefits of shared supervision. Furthermore, we introduce a prototype-based segmentation head with orthogonality regularization to encourage disentangled anatomical representations, and employ homoscedastic uncertainty weighting to dynamically balance task contributions during training. Our analysis shows that robust trait representations serve as a critical bridge for transferring knowledge to ultra-rare species. Extensive experiments on the Fish-Vista benchmark demonstrate that FISHER achieves 97.7% mAP for unseen trait identification and improves ultra-rare species classification accuracy by 13.4% over strong baselines, highlighting the effectiveness of gradient-decoupled hierarchical learning for long-tailed biodiversity recognition.

Trajectory datasets used in ADAS evaluation are heavily biased toward routine driving; genuine vehicle-to-vehicle conflict events are rare, and the rarer the event, the higher the cost when an ADAS system fails to handle it. Existing generative approaches address this imbalance by conditioning on scene-level properties - spatial goals, agent structure, or natural-language adversarial objectives - but none can accept a target Time-to-Collision (TTC) value as input and be held to producing it within a measurable error. This paper introduces SevDiff, a severity-conditioned denoising diffusion probabilistic model (DDPM) that accepts a requested minimum TTC value as a scalar conditioning signal and generates paired vehicle interaction trajectories whose realized conflict severity matches the request, evaluated through a hit-rate metric. Trained on 468 interaction windows extracted from the UTE SQM-W-1 expressway weaving-section dataset (1,041 vehicles, 822,691 observations after smoothing), SevDiff achieves 100% hit-rate within +/-0.5 s for TTC targets of 0.5-1.5 s and 97-99% at 2.0-2.5 s, with graceful degradation to 39% at TTC = 5.0 s. Generated kinematic features are physically plausible, with a maximum out-of-range rate of 4.7% across 12 features and no negative speed or gap values in more than 96.5% of samples. The hit-rate degradation pattern is physically interpretable as the strength of the conditioning signal relative to the training prior, making it a precision characterization of the generator rather than a pass/fail result.

Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster response. However, the complex coupled dynamics among drones, cables, and payloads pose significant challenges, and existing approaches remain limited in safety and scalability, particularly in dynamic and unstructured environments. In this work, we propose a learning-based framework for safe and scalable multi-drone cooperative payload transport. We introduce a minimal 2D abstraction that preserves the task-relevant drone-payload coupling required for coordination and safety, while remaining computationally efficient for large-scale learning. Using domain randomization over team size and physical parameters, we train a fully distributed policy via Discrete Graph Control Barrier Function Proximal Policy Optimization (DGPPO), enabling robust zero-shot sim-to-real transfer without fine-tuning. Extensive real-world evaluations demonstrate that a single learned policy generalizes across varying team sizes and task scenarios. Furthermore, multi-group hardware experiments show that the same policy can safely operate in dynamic environments, where other drone teams act as moving obstacles. These results indicate that the proposed framework enables efficient, safe, and scalable multi-drone payload transportation with strong generalization to complex real-world conditions.

We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs into end-to-end policy training requires embedding a quadratic-program-based safety filter as an optimization layer, but computational and differentiation bottlenecks have largely restricted prior approaches to low-dimensional systems, typically with at most 16 state dimensions. We address this limitation by combining operator splitting with the recently developed Jacobian-Free Backpropagation (JFB) method to enable scalable end-to-end training while preserving hard safety guarantees through the CBF safety filter. We justify this training methodology theoretically using nonsmooth analysis techniques and demonstrate its effectiveness on high-dimensional multi-agent nonlinear control problems with state and control dimensions up to 1200 and 400, respectively.

Continuously recorded high-resolution waveform measurements provide rich information about fast power system dynamics. However, they require automated methods to identify events. This problem is addressed by developing a spectrogram-based framework to jointly detect, localize, and classify events in real-world continuously recorded waveforms at the terminal of an Inverter-Based Resource. We recast this problem as a temporal object detection problem on spectrogram images, as they capture the transient and harmonic signatures more explicitly than in raw waveform data. Each time-series waveform is transformed using the short-time Fourier transform, and the resulting per-channel spectrograms are stacked as a tensor for event detection. We benchmark this method against a detector operating directly on raw time-series measurements. Experiments on single-phase disturbances and three-phase faults demonstrate that the proposed spectrogram method consistently improves event detection, localization, and classification over the raw waveform baseline.

Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feedback. Specifically, at each time-step, an adversary can perturb both the reward and state observations of the learner following the Huber contamination model. To defend against such data corruption, we propose {\texttt{BR-Async-Q}}: a novel, epoch-based, robust \(Q\)-learning algorithm built upon two key ideas: (i) partitioning the online data stream into batches to reduce variance, and (ii) constructing robust estimates of the Bellman optimality operator using such batched data. We prove a high-probability $\ell_\infty$ error bound for {\texttt{BR-Async-Q}} that matches that for vanilla \(Q\)-learning, up to a small additive term that scales with the fraction of corrupted samples. To our knowledge, this provides the first robustness guarantee for asynchronous \(Q\)-learning subject to both reward and state corruption. Furthermore, when only rewards are corrupted, the dependence of our algorithm's bound on the corruption fraction is minimax optimal.

In this paper, we study the control co-design (CCD) synthesis problem for a class of systems with parabolic partial differential equation (PDE) dynamics. We first derive a sufficient stability condition for the PDE. By spatially discretizing the PDE and using the sufficient stability condition, we propose a computationally tractable approximate CCD problem. We solve the approximate CCD problem using a gradient-based method. Finally, we justify our proposed approach through an example.

We present VibeVoice-ASR-BitNet, a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs. We apply heterogeneous quantization tailored to the computational characteristics of each stage: the VAE acoustic tokenizer uses full-pipeline INT8 quantization (I8_S) with kernel fusion and SIMD optimization, while the autoregressive language model adopts BitNet-style ternary weights (I2_S). To preserve accuracy under aggressive compression, we employ a progressive quantization-aware training strategy. For inference, we implement custom SIMD kernels and fused operators within the ggml framework targeting both ARM and x86 platforms, achieving real-time recognition with RTF < 1 using as few as 3 CPU threads. VibeVoice-ASR-BitNet is 1.6-2.3x faster than this http URL at comparable model sizes (~1.6 GB), with only modest accuracy degradation compared to the FP16 baseline.

Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observations. We formulate SFL as a statistical learning problem in which optimal subgraph operators are inherently data-dependent. To address the difficulty of directly estimating such operators, we develop a subgraph filter algebra based on distance-aware Laplacian constructions, defining a structured and controllable class of filters for effective approximation. We further establish performance risk bounds under the least squares loss, quantifying how well the learned operator approximates the restricted ambient mapping. Experiments real-world datasets show that, for SFL tasks, the proposed algebraic models consistently outperform polynomial filters, distribution-agnostic operators, and direct numerical filter learning baselines that attempt to recover the underlying structure from data.

Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and traffic signs, along with their connectivity relationships. Due to the lack of explicit markings for centerlines in real-world environments, the detection of centerline instances remains a significant challenge. To tackle this problem, we propose HGeo-TopoMap, which leverages an explicit prior map and implicit spatial relations to hierarchically boost topological mapping. First, a geometric adaptive learning module is designed for the road structure map obtained via inverse perspective mapping. This module discretely encodes semantic and spatial features from the map, followed by a prior-mask attention mechanism that selectively focuses on informative regions. Then, a geometric consistency learning module is devised, which leverages the geometric properties and spatial relationships of centerlines. Built on the geometry-aware decoder, it enforces spatial consistency by aligning features of centerline instances with identical geometric orientations. The proposed method is evaluated on the OpenLane-V2 dataset across the centerline, lane segment, and robustness benchmarks. Beyond substantial improvements in topological mapping accuracy, the proposed method offers the benefit of enhanced robustness, consistently outperforming baselines under both standard and challenging conditions. The source code and model weights will be made publicly available at this https URL.

We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable controlled-variable (MV-CV) pairing, controller specification, closed loop simulation, scenario generation, performance evaluation and Bayesian-optimization (BO) based tuning. Generated artifacts are executed and validated before downstream tasks proceed, and failed artifacts are repaired using validation feedback. The proposed approach is demonstrated on a nonlinear gas-preheater benchmark with coupled pressure and temperature dynamics. The generated workflow produces a physically consistent decentralized PI (proportional-integral) feedback-feedforward control structure and an executable tuning environment. Bayesian optimization reduces the closed loop performance objective, which aggregates set-point tracking and disturbance-rejection errors for the controlled variables, by approximately 26.5% relative to the initial controller generated by the workflow, mainly through improved pressure-loop transient performance. This figure quantifies the automated tuning stage rather than a comparison against a manually designed controller. The results demonstrate the feasibility of using structured large-language-model-based code generation to construct executable control-design workflows, while also highlighting the need for broader validation on larger plantwide-control benchmarks.

Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.

Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printing. Deep learning models give prospects for automation. However, training robust deep learning models, such as YOLO or Vision Transformers, is heavily hindered by the extreme scarcity of real-world industrial defects images. To overcome this limitation, this paper introduces a novel synthetic data generation framework tailored for rotogravure printing quality control. The proposed pipeline automatically generates high-fidelity images of specific printing defects (creases, streaks, misregistration, etc.) and outputs corresponding bounding boxes and annotations. To validate the framework, a synthetic dataset of 7533 images was generated and used to train the state-of-the-art object-detection model RFDETR. Experimental results demonstrate that the model trained on our synthetic data achieves a Mean Average Precision (mAP) of 80.9\% on real industrial testing samples. This framework provides a zero-cost, rapid-deployment solution for automating defect inspection in printing lines without requiring massive manual data collection.

Inspired by the widespread concept of Lyapunov-Krasovskii functionals of complete type, this article proposes an alternative class of functionals, termed Lyapunov-Krasovskii functionals of robust type. Their construction aims to improve resulting robustness bounds of linear systems with a constant delay. These refer to bounds on nonlinear or uncertain terms that can be added to the system without compromising the proof of stability. The article derives, via the classical Lyapunov-Krasovskii theorem, an $H_\infty$-norm-based small-gain robustness result established by a functional of known structure. While complete-type functionals are related to infinite-dimensional Lyapunov equations, the proposed functionals are related to infinite-dimensional algebraic Riccati equations. Existence of the functional relies on the operator-valued Kalman-Yakubovich-Popov lemma, which is made applicable due to a splitting approach. In particular, for any asymptotically stable nominal system, there exists a Lyapunov-Krasovskii functional of robust type that yields a nonzero bound on admissible perturbations. An example illustrates the reduction in conservatism compared with complete-type functionals.

Accurate evaluation of stitched image quality is essential for advancing stitching algorithms, yet existing objective metrics often diverge from human perception because they insufficiently capture stitching-specific artifacts such as ghosting and misalignment. To address this limitation, we propose SI-FID, a noise-aware extension of the Fréchet Inception Distance tailored for stitched-image assessment. Instead of modifying the FID formulation itself, SI-FID adapts the underlying feature representation through contrastive fine-tuning with controlled perturbations introduced via data augmentation, thereby enhancing sensitivity to subtle stitching-induced distortions. A pre-trained InceptionV3 encoder is calibrated using both original and perturbed samples, yielding a perceptually aligned feature space for distribution-based quality evaluation. Experiments on two complementary benchmark datasets demonstrate that SI-FID improves rank correlation with human subjective scores by over 25\% relative to conventional metrics, providing a more reliable and perceptually consistent indicator for stitched image quality.

In this paper, we propose CPSNet, a label-free deep unfolding framework for lung ultrasound image analysis. CPSNet unfolds a Cauchy proximal splitting algorithm into a forward-backward style network architecture, incorporating skip connections to iteratively enhance noisy Radon domain images. We introduce the Radon-Based Neighbor Reconstruction Loss, a novel loss function that enforces reconstruction consistency between subsampled and reconstructed image pairs in the Radon domain, while applying regularization terms to enhance robustness against noise. Integrating the Cauchy penalty into the loss function preserves the statistical influence of the prior while enabling the network to learn flexibly. Trained in an unsupervised manner without ground truth images, CPSNet is evaluated using structural similarity index, Proxy peak signal-to-noise Ratio, and relative L2 norm, demonstrating stable and effective performance in lung ultrasound image reconstruction. Applied to B-line detection, CPSNet achieves greater stability, adaptability and efficiency compared to traditional and object detection methods, effectively preserving line structures and minimizing false detections. This study underscores CPSNet's potential as a reliable and efficient solution for lung ultrasound-based diagnostics, supporting more accurate and efficient clinical decision-making. The code developed for this paper is available at this https URL.

Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipulate actuators. Dynamic watermarking can reveal such tampering, but current schemes assume linear-Gaussian dynamics and use constant watermark statistics, making them vulnerable to the time-varying, partly proprietary behavior of MTCs. We close this gap with DynaMark, a reinforcement learning framework that models dynamic watermarking as a Markov decision process (MDP). It learns an adaptive policy online that dynamically adapts the covariance of a zero-mean Gaussian watermark using available measurements and detector feedback, without needing system knowledge. DynaMark maximizes a unique reward function balancing control performance, energy consumption, and detection confidence dynamically. We develop a Bayesian belief updating mechanism for real-time detection confidence in linear systems. This approach, independent of specific system assumptions, underpins the MDP for systems with linear dynamics. On a Siemens Sinumerik 828D controller digital twin, DynaMark achieves a reduction in watermark energy by 70% while preserving the nominal trajectory, compared to constant variance baselines. It also maintains an average detection delay equivalent to one sampling interval. A physical stepper-motor testbed validates these findings, rapidly triggering alarms with less control performance decline and exceeding existing benchmarks.

Stacked intelligent metasurfaces (SIM) enable fine-grained wave-domain signal processing, but their wideband deployment is impeded by two structural factors: (i) a single, quasi-static SIM phase tensor must adapt to all subcarriers, and (ii) multiuser scheduling changes the subcarrier activation pattern frame by frame, requiring rapid reconfiguration. To address these, we propose a SIM-enhanced wideband multiuser transceiver built on orthogonal frequency-division multiplexing with index modulation (OFDM-IM). The sparse activation of OFDM-IM confines high-fidelity equalization to the active tones, effectively widening the usable bandwidth. To make the design reliability-aware, we directly target the worst-link bit-error rate (BER) and adopt a max-min per-tone signal-to-interference-plus-noise ratio (SINR) as a principled surrogate, turning the reliability optimization tractable. For frame-rate inference and interpretability, we propose an unfolding projected-gradient-descent network (UPGD-Net) that unrolls across the SIM's layers and algorithmic iterations with a learnable per-iteration step size. Simulations demonstrate that the proposed framework achieves fast convergence and significant BER gains over fully-digital baselines. Notably, the design exhibits superior robustness against errors and outperforms large-aperture hybrid precoding benchmarks in both sum rate and energy efficiency. By combining structural sparsity with a BER-driven, deep-unfolded optimization backbone, the proposed framework effectively resolves the key wideband deficiencies of SIM.

This work presents a novel structured family of Grassmannian constellations for multiple-input multiple-output (MIMO) noncoherent communications over Rayleigh block-fading channels, where neither the transmitter nor the receiver has channel state information (CSI). The proposed constellation design is built upon the geodesic curves of the Grassmann manifold, thereby exploiting its underlying geometric structure. The resulting solution is limited in spectral efficiency (with a maximum constellation size of $4M^2$ points, where $M$ is the number of transmit antennas), targeting a rate in the range of $0.25$-$1$ bps/Hz. However, all space-time matrices resulting from this design exhibit the remarkable property of having a single nonzero entry per row, meaning that only one transmit antenna is active per time slot. This property significantly reduces hardware complexity and implementation cost, while also lowering power consumption, as only a single radio-frequency (RF) chain and power amplifier are required for transmission. Furthermore, within the constellation size limits, the proposed design achieves error performance comparable to state-of-the-art optimization-based unstructured designs, as validated through symbol error rate (SER) numerical results. It also enables simple yet effective bit labeling, confirmed by comparisons of bit error rate (BER) and SER, and reduces the computational complexity of the maximum-likelihood (ML) detector for Grassmannian constellations by a factor of $M$.

We address the design of a model predictive control (MPC) scheme for large-scale linear systems using reduced-order models (ROMs). Our approach uses a ROM, leverages tools from robust control, and integrates them into an MPC framework to achieve computational tractability with robust constraint satisfaction. Our key contribution is a method to obtain guaranteed bounds on the predicted outputs of the full-order system by predicting a (scalar) error-bounding system alongside the ROM. This bound is then used to formulate a robust ROM-based MPC that guarantees constraint satisfaction and robust performance. Our method is developed step-by-step by (i) analysing the error, (ii) bounding the peak-to-peak gain, an (iii) using filtered signals. We demonstrate our method on a 100-dimensional mass-spring-damper system, achieving over four orders of magnitude reduction in conservatism relative to existing approaches.

The rapid development of sixth-generation (6G) wireless networks requires seamless integration of communication and sensing to support ubiquitous intelligence and real-time, high-reliability applications. Integrated sensing and communication (ISAC) has emerged as a key solution for achieving this convergence, offering joint utilization of spectral, hardware, and computing resources. However, realizing high-performance ISAC remains challenging due to environmental line-of-sight (LoS) blockage, limited spatial resolution, and the inherent coverage asymmetry and resource coupling between sensing and communication. Intelligent reflecting surfaces (IRSs), featuring low-cost, energy-efficient, and programmable electromagnetic reconfiguration, provide a promising solution to overcome these limitations. This article presents a comprehensive overview of IRS-aided wireless sensing and ISAC technologies, including IRS architectures, target detection and estimation techniques, beamforming designs, and performance metrics. It further explores IRS-enabled new opportunities for more efficient performance balancing, coexistence, and networking in ISAC systems, focuses on current design bottlenecks, and outlines future research directions. This article aims to offer a unified design framework that guides the development of practical and scalable IRS-aided ISAC systems for the next-generation wireless network.

The large-scale deployment of renewable energy sources, particularly offshore wind, requires large-scale transmission grid expansion projects to transmit the produced low-carbon power to the main demand centers. However, the planning and design of such complex projects currently lack a transparent and systematic process that system operators can follow when considering such investments in their grids. This paper identifies and classifies the main technical design constraints and considerations relevant to the planning of transmission grid expansion projects, and more specifically, electrical energy hubs. Seven key areas of interest are identified, namely network integration, HVDC technologies, costs (CAPEX, OPEX, and space requirements), electricity market design, future proofness and modular expandability, reliability-availability-maintainability, and sustainability. Each area of interest is analyzed in terms of its technical and operational relevance, with technical design constraints and considerations derived from such analysis. In addition, a hierarchical classification of the identified constraints and considerations (and therefore areas of interest) is introduced, distinguishing them between three criticality classes, namely hard constraints, main drivers, and key considerations. The dependencies between the different areas are discussed, too. Therefore, this work provides system operators and policymakers with a structured basis to support a transparent planning methodology with clear decision hierarchies for investments in transmission grid expansion projects.

Rotatable intelligent reflecting surfaces (IRSs) introduce a new degree of freedom (DoF) for shaping wireless propagation by adaptively adjusting the orientation of IRSs. This paper considers an angle-dependent reflection model in a wireless communication system aided by two rotatable IRSs. Specifically, we study the joint design of the base station transmit beamforming, as well as the cooperative passive beamforming and orientation of the two IRSs, to maximize the received signal-to-noise ratio (SNR). Under the light-of-sight (LoS) channels, we first develop a particle swarm optimization (PSO) based method to determine the IRS rotation and derive an optimal rotation in a closed-form expression for a two-dimensional IRS deployment. Then, we extend the design to the general Rician fading channels by proposing an efficient alternating optimization and PSO (AO-PSO) algorithm. Numerical results validate the substantial gains achieved by the IRS rotation over fixed-IRS schemes and also demonstrate the superior performance of the double rotatable IRSs over a single rotatable IRS given a sufficient total number of IRS elements.

We present an analytic approximation model for non-rigid point set registration, grounded in the multivariate Taylor expansion of vector-valued functions. By exploiting the algebraic structure of Taylor expansions, we construct a structured function space spanned by truncated basis terms, allowing smooth deformations to be represented with low complexity and explicit form. To estimate mappings within this space, we develop a quasi-Newton optimization algorithm that progressively lifts the identity map into higher-order analytic forms. This structured framework unifies rigid, affine, and nonlinear deformations under a single closed-form formulation, without relying on kernel functions or high-dimensional parameterizations. The proposed model is embedded into a standard ICP loop -- using (by default) nearest-neighbor correspondences -- resulting in Analytic-ICP, an efficient registration algorithm with quasi-linear time complexity. Experiments on 2D and 3D datasets demonstrate that Analytic-ICP achieves higher accuracy and faster convergence than classical methods such as CPD and TPS-RPM, particularly for small and smooth deformations.

With the rapid demand for energy, even the incorporation of bulk renewable energy sources is not entirely sufficient to meet demand besides adding supply uncertainty. Distributed Energy Resource Aggregators (DERAs) have the potential to address this uncertainty via aggregation and control of decentralized distributed energy sources, thereby acting like virtual power plants. We present a new approach that combines forecasting and model-predictive control to assign DERAs to follow net-demand patterns, while accounting for the dynamics of the aggregate energy sources and their capacity limits. Each DERA is represented as a flexible ``virtual battery" with constraints on state-of-charge and power limits. The dispatch problem is set up as a long-term model predictive control task that aims to minimize differences from desired charge levels, output ramping, and net-load tracking errors. To keep operations efficient in real time, we implement a rolling-horizon MPC, which updates decisions regularly using the latest marginal-demand forecasts. For forecasting, we present two models: linear regression and long-short term memory (LSTM) neural network. Using high-resolution CAISO net-demand data and five typical DERA types, our simulations demonstrate how well our approach tracks marginal-demand; in particular, we highlight the tradeoffs between forecasting horizon times and MPC update rate as well as the dependence on the choice of the load forecasting model. Our results also indicate a slight edge for LSTM models over linear regression for desired time shifts and horizon choices.

The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health. Conventional approaches have been hindered by poor cross-subject and cross-task generalization, owing to high inter-subject variability and non-stationary neural signals. We address this challenge with a zero-shot cross-subject decoding framework on the large-scale Healthy Brain Network dataset, benchmarking a convolutional neural network baseline, a hybrid LSTM, and a Transformer-based foundation model. To adapt the Transformer for regression while averting catastrophic forgetting, we propose a novel progressive unfreezing strategy. The baseline yielded an nRMSE of 0.9991, whereas our fine-tuned Transformer achieved 0.9799 on unseen subjects. This work advances scalable, calibration-free EEG decoding for computational psychiatry and behavioral prediction.

Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging. To address these needs, we present \textbf{WanSong}, a simple yet powerful approach for long-form, commercial-grade song generation. Unlike autoregressive (AR) and cascaded multi-stage pipelines (\eg, AR followed by diffusion), \textbf{WanSong} is a pure diffusion-based model that directly generates high-fidelity, multilingual songs up to 5 minutes and outputs dual stems (vocals and background music) in a single run. In addition, our diffusion framework enables faster inference through step-distillation, and offers an efficient pathway for fine-tuning and customization to support downstream editing tasks.

Underwater wireless optical communication (UWOC) is an enabling technology for high-throughput subsea networks, yet its long-term deployment is constrained by the finite energy budget of underwater nodes. To address this challenge, we investigate a mobile system wherein an autonomous underwater vehicle (AUV) performs joint wireless information transfer (WIT) and wireless power transfer (WPT) for a network of randomly distributed sensor nodes. This paper develops \textcolor{blue}{an integrated mission-level framework} that combines stochastic node discovery with state-aware servicing. First, we present an analytical model for node discovery based on a signal-to-noise ratio (SNR) analysis, deriving performance metrics that include the probability distribution of the discovery distance. Second, we introduce \textcolor{blue}{a threshold-based scheduling framework}, termed State-Aware Optimal Point Servicing (SA-OPS), which \textcolor{blue}{selects one of three actions according to the node's real-time energy state: preemptive charging, communication followed by charging, or communication only.} Simulations and multi-criteria decision analysis show that, \textcolor{blue}{under the considered assumptions and parameter ranges}, SA-OPS can improve the tradeoff between AUV energy expenditure and network-wide energy health relative to the adopted baseline strategies. The results also indicate that the selected charging threshold can be approximated by \textcolor{blue}{a simple state-dependent heuristic}, providing a practical guideline for autonomous energy replenishment in underwater networks.

Background and objective: A high classifier score and a plausible class-activation map (CAM) are often presented together, although neither establishes that the other is reliable. We introduce endoExplain as a reproducible protocol for auditing score-localisation discordance rather than as a new detector or explanation algorithm. Methods: Content hashing separated HyperKvasir development images from 1,000 masked images before training. EfficientNet-B0, ResNet-34 and ConvNeXt-Tiny were trained with three seeds each. Scores were temperature scaled using validation data only. Grad-CAM, Grad-CAM++, XGrad-CAM, HiResCAM and Eigen-CAM were evaluated on identical image-mask pairs, alongside random and centre baselines. Outcomes combined peak localisation, overlap, a top-20% deletion response, score-threshold sensitivity and adjustment for lesion size and centrality. The selected checkpoint was transferred without retraining to three external mask cohorts. Results: Temperature scaling reduced test expected calibration error from 0.0167 to 0.0115. Among 172 reserved images with scaled score at least 0.90, peak-outside-lesion rates ranged from 4.1% to 62.2% across CAMs. Method dependence remained evident across architectures and seeds, although method rankings were not universal. Spatial alignment and deletion response were not interchangeable. A random-deletion control also produced positive logit drops, limiting specificity claims based on deletion alone. External positive-mask results were dataset dependent. A source-category audit also exposed that 149/155 test positives were dyed-lifted polyps, materially bounding classifier claims. Conclusions: endoExplain makes calibration, spatial agreement, perturbation response and transfer separately inspectable. The results caution against using a score or visually persuasive CAM as evidence of lesion localisation or model reasoning.

We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and construct taxonomies of deepfake generation and detection methods, illustrating the important groups of methods. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing detectors on the most popular datasets. In addition, we develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content. The results indicate that state-of-the-art detectors fail to generalize to deepfakes generated by unseen generators. Our project page and new benchmark are available at this https URL.

Vitalic is an electronic music producer who has been active since 2001. Vitalic's 2005 track "No Fun" features a main synthesiser part built from a sequence of single inharmonic tones that evoke two simultaneous melodies. This part serves as a starting point for examining Vitalic's use of tones that do not operate on the principle of acoustic resonance. The study considers tones that evoke two or more simultaneous pitches and examines various inharmonic partial layouts. Examples outside Vitalic's music are also provided to suggest that similar tone properties can be found elsewhere in contemporary popular music.

Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel inference that estimates complete CSI directly from multimodal observations, including camera images, LiDAR point clouds, and GPS coordinates. In contrast to prior studies that rely on predefined channel models, we develop a data-driven framework that formulates the sensing-to-channel mapping as a cross-modal flow matching problem. The framework fuses multimodal features into a latent distribution within the channel domain, and learns a velocity field that continuously transforms the latent distribution toward the channel distribution. To make this formulation tractable and efficient, we reformulate the problem as an equivalent conditional flow matching objective and incorporate a modality alignment loss, while adopting low-latency inference mechanisms to enable real-time CSI estimation. In experiments, we build a procedural data generator based on Sionna and Blender to support realistic modeling of sensing scenes and wireless propagation. System-level evaluations demonstrate significant improvements over pilot- and sensing-based benchmarks in both channel estimation accuracy and spectral efficiency for the downstream beamforming task. The source code is available at this https URL.

Movable signals have emerged as a third approach to enable smart radio environments (SREs), complementing reconfigurable intelligent surfaces (RISs) and flexible antennas. This paper investigates their potential to enhance multi-user wireless systems. Focusing on two-user systems in line-of-sight (LoS), we characterize the capacity regions of the multiple access channel (MAC) and broadcast channel (BC). Interestingly, movable signals can dynamically adjust the operating frequency to orthogonalize the user channels, thereby significantly expanding the capacity regions. We also study frequency optimization, constraining it in a limited frequency range, and show that movable signals provide up to 45% sum rate gain.

Audio language models process input audio into rich frame-level representations, but the standard approach to temporal localization generates timestamps as sequences of text tokens, which discards the frame-level representations in favor of autoregressive decoding. However, generating timestamps as tokens is slow and not parallelizable, and tends to hallucinate when producing timestamps outside the training distribution. We propose internal frame-level reuse, a method that trains audio LMs to reuse their own internal audio representations for temporal localization directly, bypassing token generation altogether. We introduce a lightweight prediction head trained via different frame-level objectives: a binary frame classifier and a novel inhomogeneous Poisson process (IHP) loss that models temporal event intensity. Across word localization, speaker diarization, and event localization tasks, our approach can achieve a >50x inference speedup over token-based generation and demonstrates robust length generalization, maintaining high accuracy on out-of-distribution audio durations where token-based models collapse completely. We find that reusing audio frame-level representations yields localization accuracies comparable to (and often better than) finetuned token-based baselines.

Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts. Benefiting from the rapid advancement of joint audio-video generation, this paper proposes a more compelling new task: sync audio-video customization, which aims to synchronously customize both video identity and audio timbre. Specifically, given a reference image $I^{r}$ and a reference audio $A^{r}$, this novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user-provided textual prompts. To this end, we propose OmniCustom, a powerful DiT-based audio-video customization framework that can synthesize a video following reference image identity, audio timbre, and text prompts all at once in a zero-shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self-attention layers within the base audio-video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large-scale, high-quality audio-visual human dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity. Project page: this https URL.

This tutorial provides a critical review of the practical application of Control Barrier Functions (CBFs) in robotic safety. While the theoretical foundations of CBFs are well-established, I identify a recurring gap between the mathematical assumption of a safe controller's existence and its constructive realization in systems with input constraints. I highlight the distinction between candidate and valid CBFs by analyzing the interplay of system dynamics, actuation limits, and class-K functions. I further show that some purported demonstrations of safe robot policies or controllers are limited to passively safe systems, such as single integrators or kinematic manipulators, where safety is already inherited from the underlying physics and even naive geometric hard constraints suffice to prevent collisions. By revisiting simple low-dimensional examples, I show when CBF formulations provide valid safety guarantees and when they fail due to common misuses. I then provide practical guidelines for constructing realizable safety arguments for systems without such passive safety. A crowd-navigation simulation study further illustrates that CBF-derived reward shaping in reinforcement learning can improve empirical behavior without establishing formal safety. The goal of this tutorial is to bridge the gap between theoretical guarantees and actual implementation, supported by an open-source interactive web demonstration that visualizes these concepts intuitively.

Non-orthogonal multiple access (NOMA) systems allowing multiple users sharing the same resource block offer significant gains in spectral efficiency which can enable the required massive access in future wireless systems. However, they face several challenges due to their sensitivity to power allocation coefficients, fading effects, and imperfect channel state information (CSI). To address these limitations, this paper proposes Hadamard-NOMA, an approach leveraging the Hadamard Transform (HT) at the source level prior to modulation. By introducing HT, the system mitigates the adverse impact of fading and CSI imperfections, reducing bit error rates (BER) and enhancing overall system reliability. Theoretical analysis and Monte Carlo simulations validate the effectiveness of this technique, demonstrating robust NOMA transmission in dynamic wireless environments. The proposed method offers a promising solution for next-generation wireless networks, ensuring more reliable performance under diverse transmission conditions. Simulation results confirm analytical predictions, demonstrating significant performance improvements over state-of-the-art T-NOMA and Usman-NOMA schemes. Specifically, for the Near user, a gain of 15 dB is achieved at a Bit Error Rate (BER) of $10^{-2}$, while the Far user benefits from a 10 dB gain at a BER of $10^{-1}$. Compared to Usman-NOMA, the proposed method provides an improvement of 15 dB for the Far user at BER $10^{-1}$. Additionally, in a two-user scenario with imperfect Successive Interference Cancelation (SIC), user 1 requires an SNR at least 14 dB lower than user 2 to achieve a BER of $10^{-3}$. These findings highlight the effectiveness of applying HT at the source stage, significantly mitigating CSI errors and making NOMA more resilient for next-generation wireless networks.

Understanding and reconstructing the 3D world through omnidirectional perception is becoming increasingly important for intelligent transportation and urban autonomous agents operating in shared spaces. However, existing vision-based 3D occupancy prediction methods are constrained by limited perspective inputs and a predefined training distribution, making them difficult to apply to embodied agents that require comprehensive and safe perception of scenes in open-world exploration. To address this, we present O3N, the first framework for open-vocabulary occupancy prediction from a single omnidirectional RGB image. O3N embeds omnidirectional voxels in a polar-spiral topology via the Polar-spiral Mamba (PsM) module, enabling continuous spatial representation and long-range context modeling across 360°. The Occupancy Cost Aggregation (OCA) module introduces a principled mechanism for unifying geometric and semantic supervision within the voxel space, ensuring consistency between the reconstructed geometry and the underlying semantic structure. Moreover, Natural Modality Alignment (NMA) establishes a gradient-free alignment pathway that harmonizes visual features, voxel embeddings, and text semantics, forming a consistent ``pixel-voxel-text'' representation triad. Extensive experiments on multiple models demonstrate that our method not only achieves state-of-the-art performance on QuadOcc and Human360Occ benchmarks but also exhibits remarkable cross-scene generalization and semantic scalability, highlighting the potential of O3N for scalable open-world 3D scene understanding. The source code will be made publicly available at this https URL

We consider the problems of computing the optimal rank-1 Hankel and Toeplitz-structured approximation of arbitrary matrices under L2 and L1-norm error. Such problems arise naturally in engineered systems, including the basic few-shot signal Direction-of-Arrival (DoA) estimation problem that is of importance to modern autonomous systems applications. We develop accurate and computationally efficient structured matrix decomposition algorithms for both formulations and then derive analytically grounded small-sample-support DoA estimators for practical sensing system deployments. The resulting estimators under the L2 and L1 norms are formally shown to be maximum-likelihood optimal under white Gaussian and Laplace noise, respectively. The estimators are further validated through extensive simulation studies and real-world data experiments in few-shot DoA inference.

The 3GPP Vehicle-to-Everything (V2X) resource allocation framework defines two entity classes --- the base station and the vehicle User Equipment (UE) --- and four modes across LTE and NR generations. We demonstrate that this binary taxonomy is structurally incomplete. Base station-led scheduling saturates at high-density traffic nodes, producing latency-tail failures even when mean Packet Delivery Ratio (PDR) meets the service-class target. UE autonomy is categorically incapable of pre-emergence warning for occluded participants, and insufficient for large-scope cascading environmental hazards. We propose Mode 0, a new 3GPP V2X category whose defining entity is the Roadside Computing Unit (RCU) --- an infrastructure ensemble integrating elevated sensing (Seeing), sidelink communication (Speaking), and local computational evaluation (Thinking) under traffic-management ownership. Mode 0 defines a subfamily spectrum: Mode 0a (all-passive UEs, the guaranteed minimum) through Mode 0c (all-active UEs, the optimal target). Convergent deployment evidence across Chinese national standards, operator infrastructure (China Unicom RS-MEC), and European and US C-V2X programs confirms both institutional sides converging on the roadside traffic node without a coordination standard. A fifteen-run Multi-Agent Proximal Policy Optimization (MAPPO) simulation programme validates the architectural family: Mode 0a in the shared-pool baseline sits at the analytical symmetric-Nash coordination floor, provably unbreakable by shared-actor architecture; Mode 0c with demand separation achieves strict Pareto improvement for both classes (M0 PDR 0.999, M1 PDR 0.998), and lifts the worst-TTI PDR from near-zero to 0.601 --- the only configuration satisfying the 95th-percentile latency requirement as a structural property. We call for a 3GPP study item on Mode 0 within the NR-V2X sidelink enhancement work programme.

The image-source model (ISM) is a widely adopted method for efficiently simulating acoustic room impulse responses (RIRs) under specular reflection assumptions. Acoustic paths between source and receiver are traced to lattice points computed from successive reflections over bounding planes of the room. Rectangular rooms bound the total number of image-sources to be polynomial in the RIR's duration or distance $k$ equivalent, with degree equal the number of room dimensions $N$. Direct ISM simulations are therefore compute upper-bound by $O \left ( k^N \right )$, and consider only cases of $N \leq 3$ for tractability and real-world applications. This work proposes an alternative computational method that lowers the asymptotic compute bound to $O \left ( N k^2 \log k \right )$ for integer coordinates and room dimensions via reducing ISM lattice point counting to the classic Gauss circle problem (GCP). We extend the lattice counting model to frequency-dependent and reflection weighted image-sources in higher dimensions, relating solutions between successive dimensions via the convolution operator. Two constructions for realizing RIRs are presented, along with time-frequency controls, error and run-time analysis, and RIR statistics.

Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) methods are highly effective for utilizing large datasets, making them a popular choice for electrocardiogram (ECG) analysis. This work presents the Event Reconstruction Joint-Embedding Predictive Architecture (ER-JEPA), a lightweight SSL framework for multivariate time series, whose name and two-fold hierarchical structure are inspired by the diagnostic approach of cardiologists. At its core, ER-JEPA features: (1) a two-stage structure that constructs representations for each time interval and subsequently processes these representations as a univariate time series, (2) the hierarchical integration of two Joint-Embedding Predictive Architectures (JEPAs), and (3) a Vision Transformer (ViT) backbone. The structural concatenation of two JEPAs categorizes the model as a Hierarchical JEPA (H-JEPA), designed to encode multiple levels of abstract representations for enhanced prediction on complex tasks. This study reports a successful application of H-JEPA to 12-lead ECG data as a multivariate time series, alongside an analysis of the sensitivity of hierarchical representation during the pretraining stage. Furthermore, this study provides a qualitative demonstration that the intermediate representations produced by the first module of ER-JEPA excel at local feature extraction, as they are structurally free from over-smoothing. Pretrained on approximately 180,000 10-second recordings, the model achieves state-of-the-art downstream performance on the ST-MEM benchmark, with rapid computation and minimal resource usage.

We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the this http URL open dictionary of Yoruba personal names. The system takes tone-marked Yoruba text as input and produces audio output by applying a hand-crafted phonological rule system to a recorded inventory of 651 diphone units spanning five tonal variants of every consonant-vowel combination in the language. We describe the phonological architecture of the system in detail, including our complete tonal file-selection logic, our treatment of the three-way nasal disambiguation problem (oral /n/, nasalized vowel, and syllabic nasal), and the derivation of contextual rising and falling tones from level-tone input. We also present, as an orthographic contribution, the adoption of the caron and circumflex, which are symbols with prior standing in Yoruba phonological transcription, as standard single-vowel contour tone markers, integrated into the TTS normalization pipeline and the WriteYoruba keyboard input tool. The system's performance was evaluated through a listener study (N=50), with detailed results on Mean Opinion Scores (MOS) presented in Section 6. Keywords: Yoruba, text-to-speech, low-resource languages, diphone synthesis, contour tones, African language NLP, rule-based synthesis