Papers

Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under real-world acquisition variability, artifacts, and adversarial inputs. We introduce RobustSeiz, an open-source, model-agnostic framework that provides a standardized, reproducible protocol for stress-testing and comparing seizure detectors under controlled, clinically motivated distribution shifts before deployment. We standardize four public scalp-EEG corpora (CHB-MIT, TUSZ, Siena, and SeizeIT1) into BIDS-EEG trees and evaluate subject-independent detectors on held-out splits. Environment, noise, and adversarial transforms are swept over predefined hyperparameter grids. Each run reports sample- and event-level sensitivity, precision, F1, false positives per 24 h, Lead and Lag onset timing, and Monte Carlo dropout predictive agreement. RobustSeiz includes a Dockerized GPU pipeline, experiment registry, and full-evaluation and research-subset modes. We demonstrate the framework with a contemporary seizure detector on TUSZ across the complete implemented shift grid; an AWGN analysis illustrates how perturbation severity changes detection quality, onset timing, and predictive agreement. RobustSeiz provides a shared benchmarking standard for evaluating seizure-detector robustness under realistic clinical stressors, extending pre-deployment assessment beyond clean-data accuracy.

Site-specific training can improve wireless receiver performance without increasing computational complexity. However, real-world results have so far focused on fully trainable neural receivers and single-layer transmissions. We study site-specific finetuning of three receiver architectures: fully trainable neural, model-driven neural, and model-based. We train and evaluate these receivers using new measurements from a standard-compliant 5G NR testbed at ETH Zurich with dual-layer uplink transmission, including measurement campaigns conducted more than six months apart. Our results show that site-specific finetuning (i) substantially improves fully trainable and model-driven neural receivers, while resulting in only marginal gains for the less tunable model-based receiver; (ii) enables a single neural receiver jointly finetuned for single- and dual-layer transmission to closely match receivers finetuned separately for each configuration; and (iii) remains effective across measurement campaigns separated by more than six months. We also investigate site-specific linear minimum mean-square error channel estimation using covariance matrices estimated from either synthetic channels or site-specific measurements. When combined with iterative detection and decoding, site-specific channel estimation achieves the lowest error rate observed in our datasets. Our finetuning code and measurement datasets are publicly available at https://github.com/IIP-Group/site_specific_training

We present Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full-duplex dialogue synthesis. Building on a Diffusion Transformer trained with a flow-matching objective, Text-AB departs from the Audiobox system along three dimensions. First, it operates in a latent diffusion framework using DAC-VAE features that encode 48 kHz waveforms into a 25 Hz latent sequence, giving over 10x higher compression than previous EnCodec representations while improving resynthesis quality. Second, Text-AB is alignment-free: it consumes raw text via an off-the-shelf text encoder and learns text-speech alignment through cross-attention, removing the need for forced alignment and explicit duration prediction. Third, we scale model and data substantially, pretraining a 3B-parameter model on 480k hours of monolingual speech, followed by supervised fine-tuning on three downstream tasks: cross-lingual voice dubbing, full-duplex dialogue synthesis, and emotional full-duplex dialogue synthesis. At inference, Text-AB supports one-shot generation for up to ~1 min of speech and arbitrarily long-form generation via a multi-diffusion scheme, plus a multi-stage reranking strategy that enhances quality based on automated metrics. On a real-world dubbing benchmark, Text-AB delivers a step-change improvement over the latest internal dubbing system, with large gains in prosody similarity, voice similarity, naturalness, and shareability. For full-duplex dialogue synthesis, it approaches human recordings on short-form conversations and substantially outperforms the latest internal model on long-form human-likeness and expressivity, while natively modeling turn-taking, back-channeling, and emotional dynamics. For emotional dialogue synthesis, emotion conditioning significantly improves emotion alignment and emotional interaction quality over the unconditioned baseline.

Practical separation of single-channel co-frequency signals (SCCFSs) is hindered by the prohibitive computational complexity of benchmark algorithms. To address this issue, we propose a low-complexity separation framework based on sequential detection (SD), in which signal separation is cast as a sequential path search over a trellis. To support both hard-decision detection and log-likelihood ratio (LLR) extraction, we develop two algorithms within this framework: the SD-based separation (SDS) algorithm and its soft-output variant (SO-SDS). Furthermore, SDS employs a windowing strategy combined with dynamic pruning to concentrate computational resources on high-probability paths, thereby enabling efficient detection of transmitted symbol sequences. Building upon SDS, SO-SDS further incorporates a state completeness verification mechanism (SCVM) to estimate bit LLRs, thus facilitating subsequent soft decoding. Numerical results show that, compared to benchmark algorithms, SDS achieves significant complexity reduction without degrading separation performance, while SO-SDS offers notable computational savings with only modest LLR accuracy loss. Notably, the computational complexity advantage of the proposed algorithms over benchmark algorithms grows substantially with increasing modulation order.

Automatic speech recognition (ASR) remains challenging for individuals with cleft lip and palate (CLP) because of limited pathological speech data and large variations in speech characteristics across speakers and severity levels. These recognition difficulties can reduce the accessibility of voice-based human-computer interaction, particularly when cloud-based ASR services are unavailable or unreliable. This work investigates a severity-aware and edge-deployable ASR framework for improving recognition of CLP speech using Whisper-small. The model was fine-tuned using different combinations of normal and CLP speech representing mild, moderate, and severe conditions, together with a CLP-only training configuration, to examine how the inclusion of different severity levels influences recognition performance and fairness across speakers. The pretrained model produced pooled word error rate (WER) and phoneme error rate (PER) values of 62.46% and 52.72%, respectively. Severity-aware fine-tuning substantially improved performance, reducing the best pooled WER to 22.72% and the best pooled PER to 18.44%. Training with a broader representation of CLP severity levels also provided the best overall balance between recognition accuracy and performance consistency across severity groups. Deployment on an NVIDIA Jetson platform demonstrated real-time inference for all fine-tuned models, with real-time factors of 0.167-0.171 and peak GPU memory usage of approximately 566 MB. The results demonstrate that incorporating severity diversity during ASR adaptation can substantially improve recognition of CLP speech while reducing performance disparities across severity groups. The proposed approach further enables low-latency, Internet-independent speech interaction on edge devices, supporting more accessible and inclusive voice-based human-computer interaction for individuals with CLP.

Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future intelligent networks. In our prior work [1], we introduced the cooperative multi-task SemCom (CMT-SemCom) framework, in which the semantic encoder is divided into a common unit (CU) and multiple specific units (SUs) to facilitate cooperative multi-task processing. However, CMT-SemCom has been evaluated on homogeneous classification tasks on simplistic datasets, limiting its applicability to real-world perception systems. In this paper, we extend our CMT-SemCom to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset. We adopt the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables. In particular, we benchmark the proposed framework against independent single-task training, a conventional task-agnostic digital transmission, and single-encoder multi-decoder SemCom. Additionally, we investigate the impact of CU capacity on joint task performance, providing design insights. Extensive evaluations demonstrate that CMT-SemCom significantly outperforms the benchmarks.

Automatic tissue segmentation is essential for large-scale analysis of histopathology whole-slide images (WSIs), but accurate pixel-level segmentation remains challenging. Pixel-level annotations are expensive to obtain, models pre-trained on natural images may transfer poorly to histopathology, and pathology foundation models, despite their strong representations, are computationally expensive to deploy at scale. We address these challenges with a foundation-model knowledge distillation framework for efficient tissue segmentation. We first train Virchow2-based segmentation teachers, including a LoRA-adapted variant, achieving state-of-the-art or highly competitive performance across four datasets: PUMA, IGNITE, BEETLE, and a private blood vessel segmentation dataset. We then transfer response-level and feature-level knowledge from these teachers into compact student networks. Distillation consistently improves student performance over supervised training alone, producing state-of-the-art or near state-of-the-art results with substantially fewer parameters and up to ten-fold higher inference throughput than foundation-model-based segmentation networks. These results show that foundation-model knowledge can be effectively transferred to efficient segmentation models for scalable deployment without compromising performance. Models trained on the public datasets will be released through TIAToolbox.

The integration of Inverter-Based Resources (IBRs) into power systems introduces multi-timescale dynamics and oscillations which may propagate to distant areas and endanger the safe system operation. These oscillations pose a significant challenge as their source, frequency, and propagation pathways are often challenging to identify in large interconnected systems, comprising numerous synchronous machines and IBRs. For the above reasons, analytical tools that identify the sensitivity of the system to oscillations within a large frequency spectrum, affecting both voltage and frequency variables across various network locations, are of interest. This paper addresses this topic by introducing a frequency-domain framework based on linear analysis, which characterizes the sensitivity to oscillations of each network bus voltage and of each generation unit frequency. Within this framework, two quantitative indicators are proposed, namely the Frequency Sensitivity Index (FSI) and Voltage Sensitivity Index (VSI). These indexes are derived analytically from the transfer functions which relate the active and reactive power injections to each bus with the voltage and frequency variables across the network, derived from the linear Electromagnetic Transient (EMT) power system model. The proposed indices quantify the sensitivity of the system to oscillations of different type and frequency, providing insights for both oscillation detection and propagation analysis. The methodology is applied to a case study based on the modified IEEE 68-bus benchmark system under partial and full IBR penetration, while its accuracy is validated through EMT time-domain simulations using linear and nonlinear models developed in Matlab/Simulink environment.

Cybergenetic control of gene expression enables applications in synthetic biology, drug development, and biomanufacturing. Microfluidic platforms allow the parallel control of large cell populations. However, the resulting computational burden and intrinsic biological heterogeneity limit the scalability of conventional control strategies. In this work, we propose a similarity-based framework to reduce the computational requirement of controlling large numbers of dynamical systems. Building on existing data-driven methods for quantifying control-relevant similarity from input-output data, we cluster systems with similar dynamics without requiring explicit system identification. Based on this grouping, we develop a hierarchical leader-follower control architecture, where a single controller is designed for each cluster and applied to all members. This significantly reduces the number of control problems that need to be solved. Furthermore, we analyse the closed-loop behaviour within clusters and develop data-driven conditions under which the clustered closed-loop systems remain well-posed. The proposed approach is demonstrated in simulations of gene expression dynamics, showing that similarity-based grouping enables scalable and reliable control of heterogeneous biological systems.

This work presents a camera-based sensor for mapping plant growth over an agricultural field. The sensor can be used on ground-based vehicles like a tractor and relies on RTK-GNSS to correctly merge many multispectral images onto one map. NDVI is used as an index to estimate plant growth, but the approach can be used with other indices as well depending on the camera. The images from the camera are projected onto the estimated ground plane using perspective projection. This computationally simple approach allows for real-time processing of all images on the field even with low-end hardware. The results are compared to a commercially established vehicle-mounted sensor. Absolute values are hard to compare, but both sensors show similar trends. The camera-based approach also allows for filtering of ground and non-ground areas, potentially reducing the impact of crop density on the average measured NDVI.

No, the Julia programming language cannot land on the Moon - but it can play a crucial role in designing and analysing the Guidance, Navigation, and Control (GNC) algorithms required for doing so. This paper presents the development of a lunar landing simulation framework implemented in Julia at the European Space Agency (ESA), within the Argonaut lunar lander programme. ATLAS (Argonaut Tools for Landing Analysis and Simulation) is a modular suite of analysis and simulation tools that cover the complete descent and landing phase of Argonaut, integrating high fidelity translational and rotational dynamics, varying mass properties, propellant sloshing, detailed sensor and actuator models, and flight-representative GNC algorithms within a multi-rate simulation environment. The framework is intended to bridge early-phase prototyping and large-scale Monte Carlo analysis within a single environment. This work evaluates the advantages and limitations of adopting Julia compared to established GNC development practices based on the MATLAB/Simulink ecosystem. The results show that Julia provides a powerful, flexible, and high-performance environment for agency-driven research, early-phase design studies, and computationally intensive closed-loop simulations enabling large-scale, parallelizable simulations and rapid design iteration cycles.

Sixth-generation (6G) wireless networks are envisioned as AI-native systems in which semantic communication - transmitting task-relevant meaning rather than raw bits - moves beyond Shannon's classical bit-pipe model. Large language models (LLMs) dominate semantic encoding but are unsuitable for 6G user equipment and IoT devices, given prohibitive memory, energy, and latency costs. Tiny language models (TinyLMs) - compressed via TinyML techniques into kilobyte-to-megabyte memory and milliwatt power budgets - are the missing bridge between LLM-level semantic encoding and 6G edge hardware, yet no prior work systematically maps TinyML techniques onto semantic communication architectures for this purpose. This survey closes that gap through a two-axis taxonomy connecting six compression families (quantization, pruning, knowledge distillation, low-rank adaptation, neural architecture search, hybrid pipelines) to five semantic communication architectures (end-to-end joint source-channel coding, split learning, federated learning, knowledge-graph-assisted, and multi-task/cross-modal communication), synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier. Representative results include a CNN-Transformer encoder achieving 22 dB PSNR at 33.33% semantic-representation size reduction; a symbolic protocol machine reducing a neural MAC protocol from 4.55 MB to 1 KB (99.98% smaller) with zero performance loss; federated bidirectional knowledge distillation converging under joint model-and-data heterogeneity where FedAvg-style averaging underperforms; and knowledge-graph-assisted probability graphs cutting transmission energy by 65%. The survey identifies nine open research challenges for TinyLM-enabled 6G semantic communication, including two not previously articulated in the literature.

For resource-constrained wireless edge devices over bandwidth-limited fading channels, wireless image transmission using traditional separate coding suffers from the cliff-effect collapse. Prevailing deep joint source-channel coding (JSCC) based on convolutional neural networks can mitigate this issue but usually fail to preserve patch-level structures, thereby preventing adaptive per-token power allocation and limiting token-domain compensation for super-resolution (SR). To address these challenges, we propose a token communication framework with SR (TokenComSR). Specifically, we conceive a task-sensitive power allocation (TSPA) module and a signal-to-noise ratio (SNR)-conditioned token refinement module (TRM). TSPA distills training estimates of task sensitivity into inference token power weights, while TRM estimates an SNR-conditioned residual to correct channel-induced distortion in the token domain before decoding. Building on TSPA and TRM, the proposed TokenComSR pairs a Swin Transformer-based token transceiver with a receiver-side SR module for resource-constrained wireless image transmission. Simulation results confirm the effectiveness of the proposed TSPA and TRM, demonstrating improvements over separate coding and JSCC-SR baselines in both reconstruction fidelity and perceptual quality.

Robots operating in physical environments make control decisions based on uncertain sensor measurements, which can lead to unsafe or suboptimal actions. Runtime monitors that check their behavior against safety specifications must represent this uncertainty soundly. Zonotopes are a widely used representation, but continuously incorporating new measurements grows their order unboundedly, so monitors must periodically apply an over-approximating reduction. The choice of the reduction method substantially affects the zonotope's precision, yet existing approaches typically utilize a fixed method throughout the run, even though the optimal choice depends on the current state. This paper presents a Predictive Zonotope Reduction (PZR) approach, which frames reducer selection as an optimal control problem and solves it using beam-search model predictive control. Policy distillation into a small neural policy further provides substantially higher execution speed than model predictive control while maintaining improved performance, enabling uncertainty-aware runtime monitoring on resource-constrained real-time systems. We implement our approach in the RLola runtime monitoring framework and evaluate it on a 5-degree-of-freedom robotic arm simulated in MuJoCo, with sensor uncertainty modeled according to ISO 5725. Experiments on a Raspberry Pi 5 show that dynamic reduction significantly lowers false-positive rates in monitoring compared with static reduction strategies.

Many real-world networks involve interactions among three or more agents that cannot be reduced to pairwise coupling, making hypergraphs a natural modeling framework. In this work, we study complete synchronization in directed hypergraphs of nonlinear agents with parameter mismatches under dynamic diffusive coupling. Although proportional-integral coupling schemes are known to achieve consensus in heterogeneous linear networks, their ability to enforce complete synchronization in nonlinear systems is more limited, generally yielding only bounded synchronization. We show that complete synchronization can be attained only when the effect of parameter mismatches is structurally equivalent, in the transverse dynamics, to a constant disturbance. Under this condition, we derive invariance and local stability conditions for the synchronization manifold and develop a Master Stability Function framework for directed hypergraphs with dynamic coupling. The proposed approach accounts for distinct proportional and integral hypergraph layers and provides spectral criteria for predicting synchronization regions. Numerical simulations on directed hypergraphs of Lorenz oscillators validate the theoretical predictions and show how the integral action can compensate destabilizing effects induced by the proportional layer. We further demonstrate the applicability of the framework to a pinning-control problem in nonlinear opinion dynamics, where dynamic diffusive coupling achieves complete leader-follower consensus in the presence of heterogeneity.

Well-being is a broader concept ensuring psychological, social, physical, and cognitive health. Recreational activities are one of the major approaches to facilitate well-being. There are various recreational activities in each of the domains in which well-being can be measured. Although well-being is often assessed using subjective scales, physiological responses such as heart rate, heart rate variability, skin responses and brain activity data provide objective ways to estimate the level of well-being. Research has focused on using these measurable physiological signals to develop AI driven well-being analysis concepts. This study presents a review of previous studies that have considered analyzing well-being. This review summarizes studies on the basis of recreational activities, data collection scenarios, subjective and objective data, data analysis methods and evaluation criteria. Furthermore, this study also proposes a framework to enhance data collection, analysis and visualization setup for the assessment of well-being.

Background. Remote photoplethysmography estimates the cardiovascular pulse from facial video, and its explanations have rested on inspecting heatmaps rather than on quantitative evidence about where a model reads it. We quantified the explanations and asked whether such explanations transfer between datasets and track model performance. Method. We trained eight condition-specific RhythmFormer models on NCKU-rPPG, recorded under three illumination levels, speaking, rotation, and cycling, estimated one heart rate per 5.12-second clip, and set them beside a UBFC-rPPG reproduction. Raw attention, rollout, attention flow, and Beyond Intuition were assessed by skin coverage and the Salience-guided Faithfulness Coefficient (SaCo). Results. Beyond Intuition ranked highest on both datasets, at median coverage 0.789 and SaCo 0.837 on Static level 3 against 0.826 and 0.917 on UBFC-rPPG; lower ranks differed. Within one participant of one condition, neither measure was related to a clip's heart-rate error, waveform correlation, or signal-to-noise ratio on either dataset: 186 of the 252 coefficients fell below $|\rho|=0.10$ and 28 reached $p<0.05$ against the 13 expected by chance. Across the eight scenarios only Beyond Intuition's coverage followed the three performance measures, at $\rho=-0.43$, $+0.57$, and $+0.43$, while the attention-only methods' SaCo ran opposite to each. It failed at 40 lux alone, its median coverage falling to 0.180 and its median SaCo to $-0.178$, whereas motion degraded the estimates far more without such a drop. Conclusions. Skin coverage and SaCo carry information complementary to the performance measures rather than a proxy for them: attributing to the skin does not guarantee an accurate estimate. What an attribution reveals about a condition is where the model looks rather than how faithfully its map is ordered.

Audio deepfake detection is commonly formulated as clip-level binary classification of single-domain audio. However, real-world manipulated audio can exhibit mixed authenticity, where genuine and manipulated cues coexist across temporal transitions, overlapping sources, or both. This setting requires not only detecting manipulated audio but also localizing the components that provide evidence for the decision. We propose ToolDF, a tool-integrated reasoning framework for mixed-authenticity audio deepfake detection. ToolDF employs an audio large language model as an orchestrator trained with supervised tool-use trajectories. It adaptively analyzes the audio scene, selectively performs source separation, routes components to domain-specific experts, and aggregates their evidence into an interpretable verdict. We further introduce a mixed-authenticity ADD benchmark covering temporal transitions, acoustic overlaps, and hybrid mixtures. Experimental results show that ToolDF achieves the best overall performance on composite-type detection, achieving macro-F1 gains of 3.72 and 14.39 points over the strongest monolithic baseline and a fixed pipeline, respectively, while providing interpretable evidence localized to temporal regions and acoustic sources. Our source code and dataset are publicly available online.

Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodiversity, particularly for destructive pests such as the Desert Locust (Schistocerca gregaria), whose breeding dynamics are tightly coupled to rapidly evolving environmental conditions. Maxent has become the dominant method for presence-only data, but its reliance on a linear combination of hand chosen feature transforms limits its ability to capture the nonlinear, temporal relationships common in ecological monitoring, where covariates such as precipitation, soil moisture, and vegetation indices evolve meaningfully over time. Standard implementations flatten time-series covariates into independent features, discarding sequential structure that carries critical signal. We introduce RNN Maxent, an extension of the Maxent framework that replaces the fixed feature dictionary with a neural network, specifically a Gated Recurrent Unit (GRU), trained end to end via backpropagation. The approach preserves Maxent's presence only statistical foundations, background normalization, and probability calibration, differing only in that the nonlinearity is learned from data rather than fixed in advance. We apply RNN Maxent to map suitable habitat for the Desert Locust using 50 day environmental time series derived from ERA5 Land, MODIS, and Sentinel 3, maintaining a 7 day gap between covariates and presence records to yield forecasting behavior. Compared against standard Maxent, RNN Maxent improves performance across metrics (ROC AUC 0.862 std 0.036 vs. 0.792; F1 0.671 std 0.056 vs. 0.590).

Lesion segmentation in medical images plays a critical role in clinical diagnosis and treatment planning. Despite significant advances, lesion segmentation remains challenging due to two major factors: (1) complex background interference; (2) diverse lesion morphology. Existing encoder-decoder based methods mainly focus on enhancing feature extraction or redesigning decoding strategies. However, they lack early prior guidance and feature reconfiguration during the encoding stage, limiting their effectiveness in handling these challenges. To address these limitations, we propose FreNet, a feature reconfiguration framework with visual priors, which performs pixel-level reconfiguration before encoding and feature-level reconfiguration during encoding for precise medical lesion segmentation. To suppress background responses, we propose an Implicit Prior Neural Network (IPNN), which models a continuous spatial field and leverages visual prior from SAM to reconfigure input image before encoding stage. To better handle diverse lesion morphology, we design a Dual-domain Feature Reconfiguration (DFR) module to progressively reconfigure backbone features during encoding stage. Within DFR, the Frequency Decoupling Module (FDM) decouples backbone features in frequency domain to enhance foreground-background discriminability, while the Spatial Localization Module (SLM) spatially relocates and improving spatial stability after frequency decoupling. Extensive experiments on 9 medical image segmentation benchmarks across three imaging modalities demonstrate that FreNet significantly outperforms state-of-the-art (SOTA) methods. On the challenging ETIS dataset, our method achieves Dice improvements of 5.0% over SOTA method and 7.2% over SAM.

Thyristor rectifiers (TRs) are cost-effective electrolysis power supplies for renewable power-to-hydrogen (ReP2H) systems, but their harmonics may violate grid-code limits. In contrast to conventional solutions that rely on higher-pulse (such as 24-pulse) rectifiers, this paper proposes an operational harmonic coordination scheme that enables low-cost 12-pulse TRs to meet harmonic requirements through coordinated operation. First, a harmonic model quantifies the effects of rectifier transformer (RCT) tap positions and electrolytic currents, enabling harmonic cancellation among multiple electrolyzers (ELZs). A two-layer framework then coordinates hydrogen production and harmonic mitigation. Hourly scheduling determines ELZ commitment within the harmonic feasible region under renewable uncertainty using stochastic programming and a modified progressive hedging algorithm, while minute-level dispatch tracks renewable power and mitigates harmonics. A decomposition algorithm separates production dispatch from harmonic mitigation to improve computational efficiency. Case studies based on real-life projects show that the proposed method increases profit by 31% over current-only regulation. Annual simulations further show that coordinated 12-pulse TRs can replace 24-pulse rectifiers for harmonic compliance by exchanging additional RCT tap actions for lower transformer investment, reducing rectification-stage cost by 37.5%.

This study experimentally investigates source-direction estimation from acoustic intensity using paired cardioid microphones, with particular emphasis on well-balanced arrangements known as tight frames. Impulse responses were measured at 6 or 24 microphone positions, and two opposed-pair spacings were examined. The resulting arrays were evaluated for a single source, coherent interference between waves arriving from two orthogonal directions, and directional tracking in the presence of interfering waves from multiple directions. The results demonstrate broadband acoustic-intensity direction estimation from 50 Hz to 20 kHz, with the 24-microphone arrangement and the shorter spacing generally providing smaller errors.

This paper investigates a dual-polarized reconfigurable intelligent surface (DP-RIS)-empowered symbiotic radio (SR) system with rotatable antennas (RAs). By reconfiguring antenna orientations, RAs can steer their radiation patterns toward desired directions, thereby effectively mitigating the double-fading effect in RIS-assisted cascaded links. However, antenna rotation not only changes the radiation direction but also alters the local polarization bases, which may result in polarization mismatch and degrade the achievable gain. This motivates a joint spatial-polarization design that simultaneously exploits directional radiation gain and polarization matching. Specifically, we formulate a transmit power minimization problem that jointly optimizes digital beamforming, RA rotations, transceiver polarization states, and DP-RIS phase shifts, subject to the primary and secondary rate requirements as well as interference temperature constraints for non-SR users. To solve this non-convex problem, we develop an alternating optimization algorithm that integrates semidefinite programming, difference-of-convex programming, and Riemannian conjugate gradient methods. Moreover, to reduce hardware and computational complexity for practical deployment, we propose two low-complexity RA designs, namely a subarray-wise shared rotation design and a discrete rotation codebook design. Simulation results show that the proposed polarization-aware RA design significantly reduces the required transmit power compared with fixed-orientation and polarization-unaware benchmark schemes. Moreover, both the proposed low-complexity RA designs achieve comparable performance with reduced rotation complexity.

Most single-channel separators estimate a source by applying a real gain to the mixture in each time-frequency bin. The optimum of that format, which the oracle masks used as bounds do not attain, is the orthogonal projection of the source onto the line spanned by the mixture, its residual set by the angle between them. Locating an estimator reduces to the block structure of a real-linear operator on stacked spectra, giving a chain of four nested classes whose three larger terms match three assumptions on the prior: zero means, circularity and absence of inter-frequency coupling. Held fixed the chain is a cascade of four orthogonal projections; refitted per frame it collapses onto its first term, attributing the whole residual to one missing real parameter per bin, the phase. When the phase posterior is symmetric about the mixture direction, the minimum mean-square estimate falls back onto the line, with gain the posterior mean of the oracle gain and excess error its variance. On MUSDB18 a posterior mean under a non-circular Gaussian-mixture prior leaves the class yet stays 11.44 dB under the per-frame ceiling, which four times as many components and 7.5x the data do not close; a closed-form gate attributes some 70% of it, in decibels, to the predicted variance. The widest fixed class stays 6.70 dB under the same ceiling. Leaving the class and minimising squared error are conflicting requests: the barrier lies in the criterion rather than in the prior.

This paper summarizes the ChinaVoices Challenge 2026, which aims to establish unified task definitions and evaluation conditions for Chinese dialect speech processing and to advance multi-dialect identification and automatic speech recognition. The challenge covers 16 dialect categories and defines two tasks: Chinese Multi-Dialect Identification and Chinese Multi-Dialect Automatic Speech Recognition (ASR). It uses approximately 320 hours of speech across the Reference Set, Open Evaluation Set, and Hidden Evaluation Set. The two tasks use the same evaluation audio, and each includes restricted-data and open-data tracks. We describe the task settings, data, evaluation metrics, and Qwen3-ASR-1.7B baseline, and analyze the leaderboard results and submitted systems. In total, 28 teams submit results, 17 provide system reports, and systems from 15 teams pass the compliance review and are included in the analysis. Most eligible systems outperform the baseline, and the official top-three order remains unchanged on the Hidden Evaluation Set for both tasks. Dialect-level results show that categories with higher identification accuracy generally have lower ASR error rates, although the tasks assess related but distinct capabilities. Leading identification systems commonly exploit dialect-discriminative acoustic representations, whereas leading ASR systems emphasize data normalization, augmentation, and auxiliary CTC objectives. These results provide practical guidance for developing and evaluating Chinese multi-dialect speech processing systems.

In this study preliminary results on the classification of EEG hyperscanning data acquired using MUSE-S consumer-level EEG headband are presented. Five pairs of subjects were involved and the recording protocol contained three two-person tetris game sessions alternating with relaxation periods. The data were segmented and ten spectral and cross-coherence features were calculated. The features were arranged into feature matrices and Convolutional Neural Network model was trained to discriminate between the relaxation and gaming. Two different feature sets - the full set and a set containing only inter subject cross-coherence features were tested. The results indicate that using the full feature set, relaxation and gaming periods were perfectly discriminated. Using only inter-subject cross-coherence features 94 % and 79 % classification accuracy for training and testing data was obtained, respectively.

This paper proposes StreamWSR, a Streamable neural Waveform-domain model for speech Super-Resolution (SR). By adopting a fully causal architecture with compact frame-level waveform representation, the proposed StreamWSR supports zero-look-ahead streaming inference while avoiding vocoder-based reconstruction and explicit phase prediction. Specifically, StreamWSR downsamples the input waveform into a compact frame-level representation using strided causal convolutions. Then, a lightweight causal long-short-term modeling backbone is employed to capture both local waveform structures and long-range historical dependencies under causal constraints. Finally, the modeled output is converted back to the waveform domain through a causal transposed-convolution and combined with the input waveform via a residual connection to generate the final high-resolution speech. Experimental results on 16 kHz speech SR show that StreamWSR achieves competitive or superior speech quality and intelligibility compared with representative waveform- and spectrum-based baselines, while maintaining a zero-look-ahead streaming advantage with only 9M parameters and 2G FLOPs.

This paper develops Taylor-informed predictive cost adaptive control (PCAC) for quadrotors with online gravity-trim adaptation. First-, second-, and third-order expansions of the nonlinear dynamics about nominal hover define sparse sampled-data dictionaries for row-wise recursive least-squares identification with variable-rate forgetting. At each step, the identified predictor is linearized at the current state, and its Jacobian is fixed over the prediction horizon. The identified vertical dynamics also estimate the vehicle mass and gravity-trim input, eliminating fixed nominal gravity compensation. Simulations with an abrupt payload change and aggressive helix tracking show that the higher-order predictors improve prediction and tracking while preserving the standard PCAC formulation.

This paper develops a Taylor-informed indirect adaptive predictive control framework for nonlinear sampled-data systems using Jacobian-frozen affine predictors. A finite Taylor expansion approximates the sampled nonlinear dynamics, and recursive least squares (RLS) identifies its polynomial coefficients online. At each sampling instant, the Jacobian of the identified map is evaluated at the current operating point and frozen over the prediction horizon, yielding an affine predictor for model predictive control. In contrast to generic nonlinear feature dictionaries, the implemented polynomial dictionary is a forward-Euler/Taylor-structure-informed reduced dictionary. Exact joint-odd symmetry eliminates even-total-degree monomials, whereas additional forward-Euler-informed pruning constitutes a deliberate model reduction. Numerical simulations on an unstable nonlinear benchmark compare different Taylor degrees. The results show that higher-order models improve tracking accuracy as the operating point moves farther from the expansion point while maintaining comparable control effort. The complete MATLAB implementation is publicly available to facilitate reproducibility.

This paper addresses the stabilization problem for Aw-Rascle-Zhang (ARZ) traffic model in the presence of an arbitrarily large input delay. The linearized ARZ model is a $2 \times 2$ hyperbolic partial differential equation (PDE) system with proximal reflection, which introduces significant analytical challenges when combined with input delays. To tackle this problem, we propose a backstepping-based boundary controller capable of stabilizing the linearized ARZ model under these conditions. The input delay is modeled as a transport PDE, which reformulates the entire system into a $3 \times 3$ hyperbolic PDE system. A backstepping transformation is designed to map the original system into a stable target system, enabling the design of a delay-compensated controller. A key technical contribution of this work is that for hyperbolic PDEs with delays, we develop a characteristic-region-wise construction for kernel functions subject to two boundary constraints and close the proof via successive approximation. Another contribution is that we utilize the small-gain theorem for input-to-state stability (ISS) of hyperbolic PDEs. Two simulations are provided to illustrate the effectiveness of the proposed delay-compensated controller: one compares it with a controller without compensation, and the other employs real traffic vehicle data to validate its effectiveness.