Papers

Radio frequency fingerprinting aims to identify transmitters by exploiting small differences in the emitted signal waveform. Common approaches exploit hardware-dependent imperfections for that purpose. This work instead investigates whether differences between digital signal processing implementations of a transmitter can be identified from their radiated signals. As a practical case study, five different software-based DSP implementations for generating a 2-FSK RTTY communication waveform are considered. In a monitoring receiver, a classifier can identify these DSP implementations using convolutional neural networks that are trained on augmented software-generated signals represented as raw IQ, power spectrum or instantaneous frequency. The trained networks are evaluated on over-the-air recordings obtained using a shortwave transceiver and a software-defined radio receiver. The best neural network combines IQ and the power spectrum representation and achieves an accuracy of up to 90%. The study further examines the influence of the signal SNR and the observation bandwidth on the classification accuracy. The results demonstrate that DSP implementations leave distinguishable characteristics in the transmitted signals and suggest that DSP-dependent fingerprinting can provide complementary information for specific emitter identification systems.

Active debris removal requires a servicer spacecraft to approach a non-cooperative tumbling object, capture it, and arrest its rotation. This sequence is conventionally divided among phase-specific controllers, each depending on knowledge of the target's inertia, a dedicated grappling fixture, or a hand-designed switch at the contact-rich seam where such pipelines are most fragile. We show that a single constrained reinforcement learning policy can execute the entire mission, driving a dual-arm free-flying servicer from free-space closing through friction-based fixture-free capture to full detumbling of a target whose mass and inertia are unknown and whose state is observed only through noisy estimates. The enabling mechanism is a smooth proximity gate applied at three loci of a constrained Markov decision process. In the reward it induces the phase structure rather than imposing it; in the action space it forecloses a degenerate grab-and-pull exploit as a hard guarantee; and in the constraint set it resolves a normalization failure of constraint-based learning, in which a constraint spanning phases that load it unequally is rendered nearly inert in the phase where precision matters most. Both the failure mode and the remedy apply to any multi-phase problem of this kind. Over 1000 randomized trials the policy succeeds in 99.5% while holding its contact-force, actuator, base-reaction and collision limits throughout, transfers zero-shot to unseen geometries and centre-of-mass offsets, and extrapolates across a tenfold range of target mass.

Designing a massive multiple-input multiple-output (MIMO) detector that is tailored to each specific deployment site can improve detection performance, yet most existing detectors, including deep-unfolding methods, adapt mainly within human-prescribed algorithmic structures. This letter proposes SiteEvo, a site-adaptive agentic discovery framework that extends adaptation from parameter tuning to detector-algorithm discovery. Rather than selecting or recombining predefined architectures, the large language model (LLM) agent synthesizes complete executable detector programs under a common design contract by combining its pretrained communication knowledge, site-specific wireless evidence, and experience accumulated during the search. Each proposal defines its own processing structure and trainable parameterization, and the parameters are optimized by a communication-system backend. The resulting high-performance detector is further structurally refined to reduce block-level computational cost under a bit error rate (BER)-preservation constraint and is finally frozen for deployment without online LLM inference. Simulations across three sites demonstrate BER reductions by factors of 2.6 to 6.0 over the best-performing competing detector at each site at 6 dB, while structural refinement reduces block-level computational cost by 16.7-34.1% under a validation-BER constraint.

This paper presents the NPU-ASLP music restoration system developed for Track 1 of the China Computer Federation (CCF) Advanced Audio Technology Competition (AATC) 2026 Challenge. The task aims to recover clean music from mobile-device-captured recordings affected by coupled degradations from playback devices, acoustic propagation, environmental noise and recording terminals. System development is further constrained by limited and imperfectly aligned real data pairs and a strict model-complexity budget. In our system, we adopt TF-MoE as the backbone for its dual-axis modeling of cross-band and temporal dependencies, whose Top-1 routing activates a single expert per token to bound computation under the strict budget. We extend it with two task-specific adaptations: a scale-aware encoder and a complex mapping decoder. The former encodes normalized complex spectra jointly with per-band log-RMS to preserve the band-energy cues discarded by band-wise normalization. The latter directly estimates the clean complex spectrum rather than masking the input, thereby repairing coupled degradations. To address the limited, imperfectly aligned real recordings, we further build a challenge-calibrated playback–propagation–recording simulator that generates 1,572.12 h of sample-aligned data across 202 acoustic conditions. The final system contains 2.951M parameters and requires 82.659 GMACs for a 10-s input. On the official blind test, it achieved -49.067 dB SI-SNR, 1.273 LSD, 2.540 FAD, and 3.495 ViSQOL. It ranked third in LSD, FAD, and ViSQOL and received the third prize in the challenge.

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, conventional receivers based on linear channel estimation and subband-averaged interference covariance reconstruction suffer serious performance degradation under strong interference. To address this issue, we propose a probabilistic framework for joint channel estimation and interference suppression using score-based prior learning. Specifically, we formulate a variational inference problem that exploits the bilinear structure of the interference by explicitly separating the interference channels from the interference symbols. To solve the resulting highly nonconvex bilinear inference problem, we develop a score-based variational inference algorithm that combines score-based turbo message passing for target-channel estimation with a reverse mean propagation framework for interference-channel inference, while the interference symbols are updated via linear minimum mean square error (LMMSE) estimation. We further design an interference rejection combining (IRC) detector with subcarrier-level covariance reconstruction based on the estimated interference channels, thereby avoiding the performance loss caused by conventional subband averaging. Numerical results demonstrate that the proposed algorithm is able to effectively suppress the interference in channel estimation, as well as to accurately reconstruct the interference subspace for interference rejection in data detection, and thus significantly outperforms existing baselines.

Neural audio codecs achieve remarkable performance but operate as closed ecosystems with rigidly co-trained encoderdecoder pairs, severely hindering interoperability. To resolve this, we propose BridgeCodec, a versatile framework that decouples these pairs by translating latent representations between mismatched, frozen endpoints. We formulate this cross-codec translation as a Schrödinger Bridge optimal transport problem, enabling robust mapping between disparate distributions without predefined noise priors. To efficiently capture longrange speech temporal dependencies, we integrate a Mambaenhanced U-Net architecture. We validate BridgeCodec on an extreme deployment scenario: mapping a lightweight 8 kHz source encoder directly to a 48 kHz target decoder at an ultralow bitrate of 1 kbps. Experimental results demonstrate that BridgeCodec achieves superior wideband reconstruction and high perceptual quality, successfully bridging heavily bottlenecked, heterogeneous codecs.

Due to the directional antenna pattern, the received signal power decreases when the target deviates from the array boresight, degrading direction-sensing accuracy. Although rotatable arrays can alleviate this problem, repeated array rotations will induce an incremental long sensing latency. To break the dilemma of harvesting the directive gain from element and making a low-latency sensing, a multi-armed array comprising multiple subarrays with different orientations is proposed to provide wide spatial coverage encompassing low-altitude and terrestrial regions while enabling multi-view direction sensing. However, the different views lead to different direction-sensing accuracies. To fully exploit the complementary information from multiple views, two fusion methods, called SNR-weighted fusion and logistic-weighted fusion, are developed. The former directly assigns fusion weights according to the estimated received SNRs, whereas the latter employs an exponential SNR mapping to further emphasize high-SNR subarrays. Furthermore, the Cramer-Rao lower bound (CRLB) is derived to provide a theoretical benchmark for the proposed multi-view direction-sensing system. Simulation results show that logistic-weighted fusion achieves better accuracy in the very low-SNR regime, while both proposed methods approach the CRLB at medium and high SNRs and maintain stable sensing performance.

Monocular depth estimation (MDE) has emerged as a cost-effective alternative to hardware-dependent 3D sensing modalities, yet predicting precise metric depth remains a persistent challenge due to intrinsic scale ambiguity, edge bleeding, and limited zero-shot generalization in unseen environments. Recent multi-modal approaches incorporate 1D/3D Single-Photon Avalanche Diode (SPAD) data to resolve these ambiguities, but they mainly treat SPAD noise as a harmful artifact required for denoising or leave the noise ignored. In this paper, we present an essentially different insight: native SPAD noise is an informative prior that implicitly encodes deterministic environmental characteristics. Driven by this paradigm shift, we introduce a novel, lightweight Test-Time Gated Attention framework by Harnessing SPAD Noise, therefore dubbed HSN. Tailored for indoor scenarios, HSN leverages the synergistic combination of 1D SPAD histograms and Gate Adapters (GAs). Through three complementary optimization phases, it generates channel-wise gated attention vectors to dynamically adjust encoder feature maps at inference time, effectively resolving ambiguous scale and boundary challenges without the heavy computational overhead or dense 3D modalities that other TTA methods suffer from. Extensive experiments on challenging real-world indoor benchmarks show that HSN achieves state-of-the-art metric depth accuracy in in-domain settings, while exhibiting even greater potential under zero-shot generalization to unfamiliar environments. Furthermore, interpretability analysis reveals that the proposed gated attention mechanism adaptively shifts attention toward deeper semantic layers under intensifying SPAD noise. Code will be made publicly available.

Haptic shared control in surgical robotics combines human-side and robot-side forces at a shared end effector while preserving operator involvement. However, the effects of geometric safety choices on task execution and force coordination remain unclear. We model the interaction as a two-agent cooperative Markov game under centralized training and decentralized execution, comparing learned human control with a task-tuned fixed-impedance baseline. This model-free multi-agent reinforcement learning framework supports joint policy adaptation without requiring a complete analytical interaction model. It compares MADDPG and rMAPPO with an epigraph-constrained approach under common geometric task definitions. In simulation, rMAPPO generally achieved the highest return and tracking performance, whereas the epigraph approach produced more conservative motions and lower geometric exposure, including no observed collision or dangerous-region exposure after geometry transfer. With real operator input, fixed-impedance-trained policies were faster and successful in all recorded trials. Policies trained with a learned partner exhibited greater command opposition, while the fixed epigraph condition produced the most aligned commands. This work provides a benchmark for studying human-control representation and constraint-handling trade-offs in haptic shared control.

The AI-RAN paradigm makes the next-generation cellular network more versatile by co-locating AI and RAN workloads on the same commodity hardware. While the GPU is a promising platform for AI-RAN realization, a generic concurrent AI application interferes with the latency-sensitive RAN on GPU compute resources. In this paper, we propose Eunoia, a dynamic real-time AI-RAN GPU scheduler that is transparent to both AI applications and the RAN workload and operates at the granularity level of the RAN slot. We deploy Eunoia onto NVIDIA Aerial AI-RAN and evaluate Eunoia on both an over-the-air 5G private network and an emulated multi-cell network. We demonstrate that in a 27-cell AI-RAN, Eunoia keeps the RAN uplink P99 latency under 3 ms while delivering higher AI performance than other RAN-safe static partitioning methods.

Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose emotion information through discrete content bottlenecks or require emotion-specific modeling. We present LiveAnon, a voice-conversion-based streaming SA system that directly conditions an autoregressive codec language model on hidden features from a pretrained streaming ASR encoder, preserving emotion without emotion-specific supervision. To reduce codec-generation latency, we introduce capped RVQ decoding, which preserves dependencies among coarse codebooks while predicting the remaining codebooks in parallel. Under VPC24, LiveAnon improves intelligibility and emotion preservation over fixed-target Stream-Voice-Anon (SVA), a strong prior streaming SA system, matches emotion-prompted SVA in emotion preservation, and achieves the strongest semi-informed privacy among streaming systems. Under VPC26, it ranks second across all metrics among the evaluated anonymization systems, demonstrating a strong privacy-utility balance.

Beat tracking, chord estimation, and structure analysis are staple tasks of music information retrieval. Their datasets are drawn largely from Western popular music, and jazz remains a repertoire on which state-of-the-art systems underperform. We present JSIR, the Jazz Standards dataset for Information Retrieval. Its 267 recordings of 44 jazz standards span 25.6 hours, 3.7 times the audio of JAAH, the closest existing resource. For each recording, we provide annotations for beat and downbeat positions, chords, and sections, all produced by a jazz musician and revised in several passes. In baseline experiments, models trained with JSIR also improve on other jazz datasets: the downbeat F-measure of a state-of-the-art beat tracker rises from 87.7% to 89.2%, and the seventh-chord accuracy of a pop-trained chord estimator on JAAH from 34% to 41%. We publicly release the annotations, the annotation tool used to create them, and a web application that displays them in sync with the audio.

Multi-frequency electrical impedance tomography (EIT) often requires specialized hardware. This challenge is especially pronounced in biomedical applications. We developed a 16-electrode EIT system using an Arduino GIGA R1 WiFi for generation and acquisition, with a discrete current source, three analog multiplexers, and basic signal conditioning. Dual cores separate waveform timing from switching and acquisition. A 3D-printed tank with silver-wire electrodes was tested at 11 frequencies (1-180 kHz). Across the 10-180 kHz analysis band, median repeat-frame signal-to-noise ratio was 48 dB, acquisition rate was ~0.18 frames/s per frequency (~60 s per sweep), and cross-session baseline drift was ~6%. KCl phantoms contained an insulating ABS cylinder or KCl-filled dialysis bags (0.001-1.0 M) spanning positive and negative conductivity contrasts. Boundary voltages were analyzed without reconstruction to estimate inclusion position, contrast polarity, and relative magnitude. Leave-one-session-out matching located ABS inclusions in 29/30 runs at 160 kHz (87-97% across the band; chance 33%). For dialysis bags, polarity was correctly identified in all 36 nonzero-contrast runs. Signed voltage projections correlated with the cylindrical contrast factor (R^2=0.68 across runs; R^2=0.89 for the aggregate fit). Concentration was ordered (Spearman rho=0.85), despite saturation above ~10-fold contrast. Bag position accuracy ranged from 6/6 at the highest contrasts to 2/6 at matched conductivity. The matched-conductivity bag produced a ~1.7% RMS perturbation against 0.4-0.6% repeat-frame variability. These results demonstrate that microcontroller-based multi-frequency EIT can support selected detection and identification tasks through direct boundary-voltage analysis, supporting future studies of muscle electrochemistry.

As speech generation and real-time spoken dialogue systems advance, neural mean opinion score (MOS) predictors are increasingly used to evaluate speech quality. However, many existing predictors are trained primarily on English read speech and generalize inconsistently across languages and speaking styles. In particular, their reliability on Korean speech has not been adequately validated. We propose KOMOS, a Korean MOS predictor for evaluating the overall quality of both read and spontaneous speech. To train and evaluate KOMOS, we collect 5,000 ratings from 29 native Korean listeners for 1,000 utterances spanning real recordings, synthesized speech, and degraded speech. KOMOS achieves an utterance-level Spearman rank correlation (SRCC) of 0.900, compared with 0.703 for UTMOS v1 and 0.767 for UTMOS v2. The inference code is publicly available at https://github.com/upskyy/komos and the trained model at https://huggingface.co/upskyy/komos.

Semantic sensing extends integrated sensing and communication (ISAC) beyond physical parameter estimation by exploiting task-oriented representations from radio echoes. Nevertheless, existing multimodal sensing remains largely limited to vision-assisted radio physical parameter estimation or typically processes the visual and radio modalities separately, leaving radio representations underutilized for visual task enhancement. Feature-level fusion enables closer cross-modal interaction, while echo interference complicates the learning of user-specific radio representations and its semantic consistency with the visual entities. To address these challenges, this paper proposes a multimodal semantic sensing framework for orthogonal frequency-division multiplexing (OFDM)-ISAC systems. We propose a radio encoder to learn radio representations oriented toward visual localization from superimposed pilot echoes, together with Variational Radio-Visual Grounding (VRVG) to promote semantic consistency between the learned radio representations and visual spatial features. The radio representations are then formulated as semantic probes to highlight the corresponding entities in the visual scene through cross-modal fusion. Experiments using real-world RCooper dataset show that the resulting synergistic radio-visual embeddings enable accurate radio-visual user matching and significantly improve both user-specific visual localization and physical parameter estimation performance. The results substantiate the benefits of radio representation learning and semantically consistent fusion for multimodal semantic sensing.

In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to traverse dense clutter, limbless vertical body bending to propel through uneven terrain, and rapid spider leg crouching to help vibration sensing of prey on a web. Across these systems, correspondence between robots and animals supports the biological plausibility of these candidate physical mechanisms and motivates better measurements and models to determine how the relevant mechanical information is sensed and used to modulate behavior.

Removing audio can shift a visual predictor's output without showing whether the task uses the intended audio information. We introduce Conditional Input and Visual Interpretation Checks (CIViC), a four-gate protocol that links an interpretation to an intervention, held-out task comparison, or endpoint control. We demonstrate CIViC on actor-disjoint face-raster tests, a synthetic audio-feature mixture, and source-video-disjoint SpeakerVid proxy tasks. In the static face-raster test, cross-actor audio replacement meets the registered detection rule with a small effect; on a separate temporal endpoint, constant-audio fits score better than audio-trained fits in four matched seeds. In the synthetic mixture, the condition-augmented fit has higher 1,000-step RMSE than the audio-only fit (0.1807 vs. 0.1707), with the gap shrinking during training. In the SpeakerVid proxy, the five-feature-zero fit (five low-level audio summaries zeroed; identity, dialogue, and condition inputs retained) has lower whole-frame grayscale RMSE than aligned audio across four held-out conditions and five paired seeds, while aligned audio has lower segment-drift error. The results show that output activation, held-out error, and endpoint controls support different interpretations across these proxy tasks.

Model predictive coverage control for multi-agent systems operates under tight communication and computation budgets, which requires it to balance the load of optimization against responsiveness to varying spatial priorities. With a fixed event threshold, the update rate varies with operating conditions, making a prescribed average update budget difficult to maintain. We propose threshold-adaptive event-triggered model predictive control (MPC), which adjusts the threshold online in proportion to the difference between the target average period and the latest inter-event interval. We prove convergence of the empirical average update period to any feasible prescribed target and establish initialization-dependent one-sided bounds on the cumulative average. A nonlinear benchmark illustrates these budget guarantees. We then apply the mechanism to coverage MPC using a task-aware statistic that captures robot motion and changes in spatial importance. In a five-agent moving-density study, the adaptive policy uses 204.7 global MPC updates on average against a target of 200, compared with 266.7 for a fixed-threshold policy using the same statistic and initial threshold. It also reduces coverage cost relative to periodic schedules with exactly matched update counts. These results demonstrate direct average-budget regulation and effective task-dependent scheduling of MPC updates.

Combining aerial robots with soft continuum manipulators promises safe, compliant interaction with the environment, but the coupled dynamics of a free-flying base and a highly deformable, underactuated arm are difficult to study without a suitable simulator. This paper describes an open-source simulation framework, built on the MuJoCo physics engine, for a quadrotor that carries a tendon-driven soft continuum arm underneath its body. The arm is modelled as a pseudo-rigid-body chain of twelve segments joined by pairs of orthogonal compliant hinges. Three tendons, spaced \SI{120}{\degree} apart and routed through every segment, bend it in any direction. A geometric tracking controller on $SE(3)$ flies the vehicle; its rotor allocation matrix is generated directly from the model description. A closed-form tendon-tension allocation keeps every tension within its limits and gives the same bending moment in every direction. We report the full set of modelling choices and parameters, and we evaluate the framework in four simulated scenarios: a \SI{36}{\second} mission with a figure-eight and a tendon-driven tip sweep, an attitude-gain study, a quasi-static characterization of the bending workspace, and an impulsive disturbance applied to the arm tip. With the nominal gains the vehicle tracks the mission with \SI{3.1}{\centi\meter} RMS position error while the arm tip swings up to \SI{37}{\centi\meter} from the body axis. The gain study shows that a low-bandwidth attitude loop lets the arm excite sustained oscillations. The simulation runs about $15\times$ faster than real time on a laptop CPU. All models, controllers and scripts that reproduce the figures are released.

Continuous-aperture arrays (CAPAs) provide a new electromagnetic transmission paradigm by directly controlling the source-current distribution over a continuous radiating aperture. This paper studies CAPA-enabled integrated sensing and communication (ISAC) with spatially extended sensing targets, for which the electromagnetic response over each target region is characterized by an aperture-to-region operator. Unlike point-target models, the resulting illumination operator may have infinite rank, precluding a priori finite-dimensional reduction of the transmit design. We formulate the joint communication and target-illumination beamforming problem in the continuous electromagnetic domain and develop an operator semidefinite relaxation (SDR). A key structural result is that the optimal transmit covariance operators remain finite rank even when the aggregate illumination operator has infinite rank. We further establish the tightness of the operator SDR and provide a constructive rank-one recovery of the information beamformers. Since the finite electromagnetic subspace supporting the optimum is generally unknown, we develop a dual-slack-driven adaptive mode-enrichment method that discovers the required modes from the full-space optimality conditions. The dual-slack violation simultaneously identifies missing modes and yields a computable certificate of the remaining full-space optimality gap. Global convergence of the enrichment sequence to the full-space optimum is established. Numerical results validate the finite-rank optimal structure and show that the proposed method approaches the full-space optimum with substantially fewer electromagnetic modes than truncated-SVD and spatial-Fourier representations.

Ultra-reliable and low-latency communication (URLLC) is essential for mission-critical cellular Internet of Things (IoT) applications requiring reliable short-packet transmission and efficient radio-resource allocation. However, finite-blocklength (FBL)-aware beamforming becomes computationally demanding as the numbers of users and antennas increase. Existing successive convex approximation (SCA) methods typically rely on general-purpose convex solvers without exploiting the structure of the resulting beamforming problem. This paper develops a structure-exploiting consensus alternating direction method of multipliers (ADMM) framework for low-complexity FBL-aware beamforming in downlink multi-user multiple-input single-output (MU-MISO) cellular IoT networks. The FBL sum rate is maximized subject to transmit-power and per-user minimum-rate constraints. Each SCA iteration yields a convex quadratically constrained quadratic program (QCQP), reformulated into an equivalent consensus problem. For an M-antenna base station serving N IoT nodes, the global update exploits Kronecker structure to replace an MN-by-MN factorization with a shared M-by-M factorization, while rank-one channel matrices enable low-complexity local projections. CVX-SCA serves as a general-purpose benchmark, whereas standard ADMM-SCA isolates the gains from structural exploitation. Numerical results demonstrate close agreement with CVX-SCA in objective value, feasibility, and convergence. Under user scaling, the proposed method achieves more than 4-fold end-to-end speedup over CVX-SCA and approximately 6-fold per-iteration speedup over standard ADMM-SCA, demonstrating its computational efficiency for cellular IoT resource allocation.

Sharing of information in multiagent systems allow for improved coordination. Delays in communication can cause asymmetries in information, causing classical results to fail. We study the decentralized LQG problem with delayed information. In general, the optimal policies are not linear and are difficult to calculate. Restricting to linear policies, we analytically calculate the gradient descent for gains from private information. This naturally lends itself to a gradient descent algorithm which we apply to examples to show policy improvements.

An incomplete inventory creates a specific attribution ambiguity in load disaggregation: unclaimed demand can offset changes in claimed loads without altering meter reconstruction. We study how the allowed target and nuisance trajectories govern this ambiguity. A bounded ordered Markov chain represents unclaimed demand, while an unweighted acceptor removes commissioning-inconsistent paths from one target without reweighting surviving paths; exact factorial MAP jointly decodes all paths. At two fully instrumented commercial buildings, restricting exterior-lighting support improves agreement on the count of rooftop compressors although no compressor chain is constrained; the gain depends on the nuisance representation. A synthetic experiment identifies the boundary: the effect persists under support-preserving changes but reverses when the true path violates commissioned support or the commissioned level is miscalibrated. Attribution therefore depends on joint target-nuisance path geometry, not reconstruction error alone.

High-fidelity audio coding requires faithful reconstruction of perceptually important high-frequency components, which are often lost due to the aggressive temporal decimation and quantization of neural codecs. State-of-the-art neural audio coding methods rely on adversarial learning to hallucinate these missing components, yielding perceptually plausible but imprecise reconstructions; a limitation for high-fidelity music applications. As a remedy, we propose high-efficiency neural audio coding (HE-NAC), which provides a multi-band extension of the popular residual vector quantization (RVQ) scheme. HE-NAC introduces an effective training mechanism that harmonizes a bitrate-free blind upsampling process, residual learning to compensate it with minimal bitrate, and a multi-head decoder architecture for band-specific reconstruction. As a result, HE-NAC achieves a balanced trade-off, reliably reconstructing the core-band ($\leq$ 3 kHz) component while preserving overall sound fidelity. Subjective listening tests and objective evaluations demonstrate that HE-NAC outperforms both the DAC baseline and MPEG HE-AAC at equal or lower bitrates.

Response-supervision timers in the 5G Non-Access Stratum (NAS) bound the time allowed for signaling exchanges before retransmission or abnormal-case handling begins. In non-terrestrial networks, that interval can include satellite propagation, terrestrial transport, and processing by multiple core-network functions. Under current 3GPP specifications, the applicable timer value for satellite access is selected according to the satellite NG-RAN radio access technology (RAT) type. However, control paths within the same low Earth orbit (LEO) network can differ in propagation delay and processing load. This article reviews the relevant 3GPP timer semantics and traces the delay components covered by different timers through 5G registration. It organizes candidate refinements along two dimensions: configuration granularity and information scope. A registration case study illustrates the trade-off between avoiding premature timer expiry and recovering promptly from message loss. The article identifies the information and coordination that future specifications would need to address to support interoperable timer configuration.

The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the attack surface. A coordinated cyberattack has the potential to disrupt distribution-system, bulk-system, and market operations. Existing cyberphysical defense strategies largely assume access to grid network models and operating conditions, which may not be available to third-party aggregators. This paper proposes a novel hybrid, perturbation-based cyber-physical defense for VPP-operated DERs that blends game-theoretic planning with model-free reinforcement learning (RL) under a Bayesian belief-update framework. Topology agnosticism is defined from the VPP information and implementation perspective: the VPP does not require access to the feeder topology, line parameters, switching configuration, or full system state. This work advances the state of the art through three contributions: (1) a multi-output Gaussian process with Hamiltonian Monte Carlo for discrete attacker-type inference, probability of detection, and confidence of detection for modeling the belief distribution; (2) moment-matched Gaussian propagation of posterior beliefs across episodes; and (3) an RL policy that adaptively integrates Stackelberg leader (VPP) actions with exploration. Furthermore, we establish equilibrium existence and derive an episode-level closed-loop utility bound connecting attacker-belief uncertainty with GT-RL action alignment. Evaluations on IEEE 13- and 123-Bus feeders with DER portfolios demonstrate effective detection under attacker-model uncertainty and evaluated feeder re-configuration conditions, while requiring no explicit feeder-topology knowledge. Overall, the results indicate that combining bi-level game-theoretic planning, nonparametric Bayesian inference, and adaptive RL is a promising direction for VPP cybersecurity.

Word-aligned prosody is increasingly used to detect depression and posttraumatic stress disorder (PTSD), yet the stability and clinical relevance of these measurements remain unclear. We examine stability at two levels. At the word level, we ask whether a speaker produces the same word in a similar speaker-specific way across different utterance contexts. At the speaker level, we ask whether averaging prosody across many words produces consistent speaker scores across separate recordings. Drawing on principles of psychological measurement, we compare these representations in four corpora spanning acted, spontaneous, and clinical speech. Word-specific deviations show little cross-context recurrence (0.01-0.16), whereas speaker summaries are highly consistent, reaching 0.997 in ESD, 0.73-0.90 in MSP-Podcast, and 0.84-0.996 in DAIC-WOZ and PTSD-STOP. Despite this consistency, associations with depression or PTSD severity do not exceed 0.15; equivalence tests exclude effects of 0.30 in all analyses and 0.20 in DAIC-WOZ, while within-person associations with daily PTSD symptoms are near zero ($|\rho|\leq0.04$). Thus, aggregation yields stable speaker-level prosodic measures, but these measures do not detectably track depression or PTSD severity in the studied corpora. Code is available at https://github.com/jen900704/word-aligned-prosody.

In this paper, we study functional-split selection for an Open radio access network (RAN) deployed as a non-terrestrial network (NTN), in which a low-Earth-orbit (LEO) satellite hosts the radio unit, so that each candidate split (7.2x, 6, 2) places the RAN at a distinct point of the transparent-to-regenerative axis. We capture the space-ground geometry exactly through an instantaneous link plane, which yields closed-form slant range, elevation, delay, and Doppler for an inclined orbit and a rotating Earth. On this geometry we derive the received-SNR density of the elevation-driven LOS/NLOS mixture channel and, through a split-dependent channel state information (CSI) aging mechanism, an uncoded bit-error-rate (BER) law for each split, establishing that what separates the splits is the freshness of the CSI each brings to its adaptation loops: a loop closed on the ground pays the feeder round-trip and incurs an aging-induced error floor. We formulate split selection as a prefer-centralized feasibility problem over reliability, aging, and fronthaul-capacity constraints, and we show that its exact solution is a closed-form region rule in four state coordinates, validated against exhaustive enumeration without a single mismatch. Because this solution is reactive whereas a split change must be initiated ahead of time, we propose a compact Kolmogorov-Arnold network (KAN)-LSTM, sized for an edge xApp/rApp, that predicts the split schedule seconds ahead; we show that it attains high schedule accuracy at a sub-percent BER-violation probability and delivers nearly all of the ground-truth policy's goodput, outperforming naive and reactive baselines.

In this paper, we consider joint beamforming and power allocation for a mobile multiuser MIMO integrated sensing and communication (ISAC) network with clutter, residual self-interference, and outdated channel state information, under per-user rate and sensing signal-to-interference-plus-noise ratio (SINR) constraints that must hold on every realization. The design must be recomputed every block, and distant or weak targets can make the sensing constraint unmeetable. We propose a physics-informed unsupervised learning (PI-UnSL) network that corrects the analytical design, meets the power and norm constraints exactly by construction, and is conditioned on closed-form interference statistics, spanning the whole tradeoff in one pass. We derive a closed-form sensing SINR at the interference-aware combiner, yielding a necessary range-radar-cross-section-power condition and a priority-independent analytic violation floor, so the problem is intermittently infeasible. We then show that a dual targeting below the attainable floor has no fixed point, and calibrate the label-free objective against the measured floor. Numerical results confirm the floor and the bounded calibrated dual, and show that PI-UnSL substantially raises per-realization joint quality of service over the closed-form baseline, delivers more rate than time-division ISAC at matched detection over most of the tradeoff, and exceeds the reliability of an uncalibrated alternating-optimization solver at a fraction of its latency. More broadly, the physical knowledge given to the network, not its architecture, makes such problems learnable without labels: five architectures fail alike from scratch and perform alike when started from the closed-form design and conditioned on its interference statistics.

We present a finite-horizon, greedy heuristic-based co-optimization and optimal control framework that jointly determines the optimized charging rates, charging times, and an optimal charging control law for electric vehicles (EVs) to improve both voltage stability and small-signal dynamic performance of a power distribution system. The proposed strategy encourages EV drivers to be flexible in choosing their charging rates and times through monetary incentivization under a fixed budget. The design is validated using the IEEE 33-bus distribution system model with ten EV charging stations (EVCSs). Results show that incentivizing drivers to settle for lower charging rates, implemented by a wait-and-save strategy, and rescheduling their charging times strategically can significantly improve the voltage stability index (VSI) of the system as well as the damping factors for the mid-frequency oscillation modes seen in the DC-link bus voltage.