Papers

Accurate bistatic radar cross-section (RCS) characterization requires near-field measurements over multiple dimensions, which, under typical near-field sampling requirements, can lead to long measurement durations for electrically large radar targets. This paper presents a near-field-to-far-field transformation based on an equivalent surface-current reconstruction using a-priori information about the target to reduce the number of newly acquired near-field samples. It is assumed that the prior target and the target under test (TUT) differ by localized geometrical modifications. Their complex differential near field is reconstructed using Tikhonov-regularized least squares QR factorization (LSQR) method. The reconstructed differential far- field contribution is coherently combined with the prior far-field contribution to estimate the bi-static RCS of the TUT. The method is preliminarily evaluated using simulations of a complex target at 6 GHz. The results indicate that, for the investigated simulation case, prior information can be used to thin out the sampling grid by 90% while maintaining close agreement with the simulation baseline.

A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data. Gradients are released in the order of L to C to rL to rC, a sequence derived from sensitivity analysis. The five-stage schedule recovers L and C to within 5% and the series ESR to 10%, while a three-stage variant attains the same accuracy 11% faster. An 84 factorial sweep confirms that the trained PINN remains accurate for simultaneous 8% perturbations of all four components, achieving a waveform RMSE of 2.6 x 10-2 over 5 ms-an order-of-magnitude better than a simultaneous-training baseline.

High penetration of single-phase distributed power production and frequent topology changes can significantly increase voltage unbalance in distribution networks. Reliable probabilistic power flow (PPF) assessment for the network, therefore, must account for topology-dependent power-flow equations, numerical error in the voltage unbalance factor (VUF), and finite-sample uncertainty in power measurements. In this paper, we propose Topology-Adaptive Certified Quantile Estimation (TACQE) to estimate the maximum VUF across monitored electric buses. Following a local network change, TACQE updates the cross-couplings among power-flow sequences, and certifies candidate PPF solutions against the updated nonlinear equations. The resulted power-flow state enclosures provide rigorous bounds on the maximum VUF. Furthermore, we derive independent reference-solver evaluations to calibrate discrepancies from the reference PPF solution. This leads to a uniform sampling bound that yields simultaneous upper-tail probabilities and quantiles, which provides a finite-sample guarantee for the estimated maximum-VUF distribution over an entire range of VUF thresholds. From our numerical simulations across 101 fixed-topology quantiles, it demonstrates that the designed TACQE can provide a fast candidate post-change power flow solution where it reuses most of the previous network model and modifies only the affected bus blocks.

This paper studies popularity dynamics in social networks, where users' engagement with competing channels is driven by intrinsic content quality, social influence, and popularity feedback. We introduce a stochastic model in which users interact through a directed influence network and channel exposure depends jointly on these three mechanisms. We first characterize the resulting dynamics in the absence of intrinsic quality, showing that popularity concentrates asymptotically on a single channel, leading to a winner-takes-all regime. When all channels have positive intrinsic quality, we establish ergodicity of the popularity process and characterize its long-run behavior. We further show that, when intrinsic quality is small, the system exhibits metastable behavior: it can remain for long periods near configurations dominated by a single channel before transitioning to another. These results highlight how weak quality differences can coexist with strong path dependence induced by popularity feedback. We then investigate platform-level interventions and propose controls that compensate for network-induced exposure biases and mitigate excessive concentration. Analytical results are illustrated through numerical simulations and an empirical analysis of YouTube data, whose observed popularity dynamics are consistent with the proposed stochastic framework. The results provide a mathematical framework for understanding the interplay between quality, social influence, and reinforcement in online popularity dynamics.

Training hardware by gradient descent requires a gradient to be computed using a separate backwards pass. If a circuit is reciprocal, meaning every connection is symmetric, the backward pass is the same network run a second time with the error as its input. The reciprocal tau-cell, a weak-inversion log-domain circuit, makes this practical in standard CMOS. Each cell is a current-mode leaky integrator with a built-in ReLU; each connection is two transistors whose signed strength is a ratio of bias currents. The equilibrium of the network is the solution to a convex program, and latching the cells that are off will hardware linearise the circuit, making its second run exactly linear. Transistor-level SKY130 simulations show that the second run gives the gradient within 1.9%; that hardware linearisation permits large, noise-robust error signals; that a 13-cell network learns the Iris data set; and that learning in situ restores mismatched circuits on which software-trained weights fail, from 53-63% to 88-96% test accuracy.

In hybrid monostatic-bistatic orthogonal frequency-division multiplexing (OFDM) sensing, each sensing node receives a superposition of monostatic and bistatic target returns, whose phase-noise (PN) characteristics differ fundamentally due to oscillator sharing. While monostatic sensing benefits from partial PN cancellation through the common transmit-receive oscillator, bistatic sensing involves independent oscillators and is therefore more strongly affected by PN. In this paper, we characterize the joint residual differential PN statistics of these superimposed components in a centralized two-node sensing system and quantify their impact on target localization via theoretical lower bounds considering three different receivers: (i) PN-free ideal receiver, (ii) PN-aware receiver that exploits the joint PN statistics, and (iii) PN-ignorant receiver that neglects PN.

We present a novel class of minimum energy estimation problems for positive linear systems with linear costs subject to coupled process and measurement disturbances, where the latter is bounded by the state and the former is bounded by the noise. Through dynamic programming we show that the problem admits explicit solutions. Remarkably, the Luenberger observer structure of the optimal estimator emerges from the optimization itself, with a switching gain that inherits the sparsity of the disturbance constraints, and it is obtained from a differential equation in the finite horizon case. In the infinite horizon case, an algebraic equation characterizes the limit of the finite-horizon solution and yields a stationary estimator with a static gain. We introduce two distinct notions of detectability to characterize when the static gain stabilizes the error dynamics while preserving their positivity, and analyze under which conditions the stationary solution can be formulated as a linear program. We further establish that a disturbance perturbation of finite L1-norm yields an estimation error of finite L1-norm. The framework is illustrated on a large-scale irrigation network.

Motion capture is increasingly important in healthcare, rehabilitation, and sports applications. However, conventional vision-based systems require camera infrastructure and line-of-sight conditions, limiting their use in daily environments. This paper proposes a location-independent platform for motion capture, based on an array of IMU (inertial measurement unit) sensor nodes without individual wiring or onboard batteries, enabled by two-dimensional communication and power delivery over conductive fabric. Pipeline processing in each sensor node, combined with a token-passing protocol, enables collision-free and stable data acquisition at 50 Hz. Real-time three-dimensional reconstruction of flexible objects is further demonstrated by applying Hermite spline interpolation to orientation data from grid-aligned IMUs. Against a cylinder of known radius, the reconstructed surface deviates by 2.60 mm RMS over the 225 mm span of the array. The reconstruction is formulated for the quasi-static regime, and this letter empirically validates it in that regime. The hardware platform and communication protocol implemented in this work enable further investigation toward dynamic motion capture textiles.

Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.

Bilinear Compressive Security (BCS) is a lightweight encryption scheme that combines compressed sensing with a fresh random convolution filter to improve resistance against key-recovery attacks under key reuse. Previous work derived information-theoretic guarantees for BCS by relating key identifiability to phase retrieval under the assumption that an adversary has access to the full ciphertext distribution induced by the random filter. In practice, however, an adversary only observes finitely many encryptions, leaving the security of BCS in the finite-sample regime largely unexplored. In this paper, we study key recovery from only finitely many observations. We first develop a covariance-based attack that estimates the linear measurements from repeated encryptions of the same plaintext and subsequently recovers the secret measurement matrix. We analyze the estimation procedure and derive finite-sample guarantees together with corresponding sample-complexity bounds. We further investigate the complete attack experimentally and compare its observed recovery behavior with the previously established information-theoretic identifiability results. Our experiments show two qualitatively different recovery regimes on the structure of the measurement matrix. For dense matrices, including Gaussian matrices, the number of measurements required for successful recovery appears to grow linearly with the signal dimension. This differs from the worst-case behavior suggested by information theory. In contrast, for matrices with sparse rows, we observe a quadratic growth consistent with the theoretical bound. These results suggest that the sparsity structure of the measurement matrix plays an important role in the security of BCS and should be taken into account when selecting parameters and designing practical instances of the scheme.

Background: Estimating subvoxel tissue fractions from magnetic resonance fingerprinting (MRF) is challenging. Conventional dictionary matching and segmentation pipelines can propagate errors from intermediate parametric maps into tissue fraction estimates. Purpose: We introduce DeepPV-MRF, a deep learning framework that directly estimates voxel-wise tissue fractions from raw MRF signals. Methods: A cascaded spatial-temporal network was trained on subject-derived Bloch simulations with radial k-space undersampling. The network combines temporal encoding and spatial U-Net refinement to estimate cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) fractions without intermediate T1/T2 maps. We compared DeepPV-MRF with partial-volume dictionary matching (PV-DM) and pseudoinverse estimation (PV-Pinv) using simulated and in vivo data. Results: DeepPV-MRF outperformed both methods across acquisition settings. In single-shot simulations, CSF/WM/GM mean absolute errors were 0.0418/0.0744/0.0757, versus 0.1281/0.2443/0.2958 for PV-DM and 0.2021/0.2793/0.2630 for PV-Pinv. Corresponding structural similarity values were 0.960/0.916/0.922. In vivo results showed sharper anatomical boundaries and smoother tissue transitions, while cross-shot analysis indicated improved consistency across volunteers. Conclusions: DeepPV-MRF enables accurate and efficient direct tissue fraction estimation from MRF signals, particularly with limited shots. Further validation is needed before clinical deployment.

Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although acoustic monitoring provides a scalable solution for machine condition monitoring, its deployment is often constrained by high false-positive rates. We propose a two-step approach to automatic anomalous sound detection: first, when only normal operating audio samples are available, a regularized unsupervised model is deployed; then, after the first machine fault, a few-shot adaptation strategy exploits the anomalous samples acquired during the fault to improve the system. This adaptation increases the area under the precision-recall curve (AUPR) from 23.0 to 64.0 and improves detection in the low false-positive-rate region, with the partial area under the receiver operating characteristic curve (pAUROC) increasing from 39.6 to 72.8. We also show that covariance regularization improves both prediction accuracy and training stability in the unsupervised model. These results provide a practical strategy for acoustic anomaly detection systems, enabling improved accuracy and reduced false alarms.

Power amplifier (PA) nonlinearity is a critical transmitter impairment in the upper mid-band (7 GHz to 24 GHz), designated as Frequency Range 3 (FR3) in emerging 6G standardisation. The 3GPP PA model baseline, TR 38.803 Annex A, was derived from 2.1 GHz and 28 GHz measurements and has not been revisited yet for FR3 standards work. This paper addresses that gap with characterisation measurements of multiple PA devices-under-test (DUTs), driven into gain compression in the 7 GHz and 15 GHz bands with 100 MHz and 200 MHz bandwidth. Memoryless polynomial and Generalized Memory Polynomial (GMP) behavioral models are extracted by least-squares regression and compared with 3GPP models spanning FR1 and FR2. The GaN PA DUT is modelled best at a higher nonlinear order and shows markedly stronger phase distortion than the wideband PA. The memory terms of the GMP model measurably improve the Normalized Mean Square Error (NMSE) at all operating points. An orthogonal frequency-division multiplexing (OFDM) ambiguity function analysis from the Integrated Sensing and Communication (ISAC) literature, using the extracted models, illustrates how PA compression raises the sidelobe floor of the sensing response. These results provide a reference for PA impairment modelling in FR3, directly applicable to ISAC system design. All model coefficients and measurement data are publicly available [1].

Existing placement studies for pinching-antenna systems (PASS) usually assume that antenna movement is instantaneous and free. In practice, actuators move at finite speeds and must pause radiation while repositioning. This makes performance depend on consecutive antenna positions, turning placement into an edge-weighted longest-path problem on a temporal trellis. We also show that an obstacle blocks a wider range of antenna positions as it approaches the user, causing sudden performance drops that make multi-step lookahead essential, and that once obstacle height is included every blockage event shadows at least a fixed minimum length of waveguide. We give an exact offline solution with complexity O(TMS) and bound the per-slot loss of a W-step receding-horizon policy by O(1/W). Measurements show that the required lookahead window is set by shadow width rather than by waveguide length. Across 20 random seeds, the exact policy delivers 17.7% higher max-min throughput than per-slot optimization under blockage, and matches it when no blockers are present.

This paper proposes a Full Duplex (FD) framework for safeguarding Integrated Sensing and Communications (ISAC) against a passive multi-antenna Eavesdropper (Eve). The adversary is modeled as both a communication and sensing threat, seeking to decode uplink data streams and estimate the surrounding scene. Eve's threat is quantified through two worst-case requirements over a location uncertainty region: a minimum secrecy rate for data confidentiality and a minimum eavesdropping Cramer-Rao Bound (CRB) for sensing privacy. We derive the eavesdropping information measures governing both data interception and scene inference, showcasing that transmitting Artificial Noise (AN) can degrade Eve's capabilities through the inverse interference-plus-noise covariance. Exploiting the underlying structure of the problem, we characterize finite-power local and robust feasibility conditions for target secrecy-privacy pairs. The analysis showcases that AN is fundamentally subspace-limited: increasing power is beneficial only along Eve's communication channel and sensing-derivative directions illuminated by the effective AN subspace. Finally, we formulate an AN power allocation problem that is solved via monotone branch-and-bound, and numerical evaluations quantify the AN power required to guarantee joint secrecy and sensing privacy.

This paper studies resilient angle-constrained leader–follower formation control and localization of multi-agent systems under asynchronous denial-of-service (DoS) attacks, which block individual sensing and communication links. To preserve the well-posedness of angle-based localization, an all-or-nothing triangle integrity protocol (ANTIP) is proposed to convert edge-wise attacks into triangle-wise ones, and DoS attacks are classified into localizability-maintained and localizability-broken attacks. First, with relative position measurements over attack-free links, resilient formation tracking laws are designed for leaders with constant velocities and with time-varying velocities comprising translational, rotational, and scaling motions. These laws guarantee asymptotic tracking and finite-time recovery of tracking, respectively, when the duration of localizability-broken attacks is suitably bounded. Second, with only angle measurements and communications, a resilient simultaneous localization and formation control (RSLAF) scheme is developed. It combines a finite-time distributed observer, which estimates the exosystem matrices and states under asynchronous DoS attacks, with a perturbation-based localization law that prevents localization from getting stuck at degenerate configurations. The theoretical results are rigorously proved and illustrated by numerical simulations.

Technologies, such as co-simulation, in particular, digital twins (DTs) are used for comprehensive and reliable prototyping, verification and validation (V&V) of control systems. There, the combination of feedback loops across multiple realities (e.g., simulations, physical setups) enables various forms of experimentation with robots and autonomous systems. However, the handling of multiple realities requires a more specific support by the modelling techniques used in control engineering. To streamline the latter in the system life cycle, we propose a framework for mixing (i.e., fusing, defusing) control input and feedback signals (i.e., stimuli, responses) in simultaneously operated plants for multiple realities.

Frequency-response-function-based controller design enables synthesis directly from measured data, avoiding the need for accurate parametric plant models. This paper presents a generalized IQC-based approach for convex synthesis of structured MIMO feedback and feedforward controllers from frequency response function data. The framework supports flexible controller parametrizations, multiple performance objectives, and both singular-value and element-wise performance shaping of MIMO controllers within a unified frequency-domain formulation. To facilitate practical deployment, the synthesis approach is embedded within a bootstrapped controller design methodology that progressively increases controller flexibility while refining the performance specifications. The resulting approach is validated experimentally on the motion stage of an industrial wirebonder, where clear performance improvement is demonstrated through MIMO $\mathcal{H}_\infty$ synthesis, element-wise feedback shaping, and dynamic feedforward design. The results demonstrate that high-performance multivariable controllers can be synthesized directly from measured FRF data in a practical and systematic manner.

This paper proposes a severity-aware machine learning framework for reducing the computational burden of preventive AC security-constrained optimal power flow (PSCOPF). The central idea is to identify a compact minimum security set (MSS) containing only the non-dominated contingencies and associated post-contingency constraints that materially influence the preventive operating point while satisfying a prescribed severity tolerance. An offline procedure combines AC security assessment, severity-based ranking, non-dominated screening, and iterative reduced PSCOPF. After each reduced PSCOPF solution, exhaustive security assessment over all credible contingencies identifies additional contingency–constraint pairs whose residual severity exceeds the specified threshold. Severity is characterized using the normalized number, accumulated magnitude, maximum magnitude, and average magnitude of post-contingency violations. The resulting operating-point-dependent MSSs are used as labels for a supervised multi-label learning model that predicts the security elements required to construct the reduced PSCOPF directly from the pre-contingency state. The proposed framework therefore focuses the optimization on the contingencies and constraints that affect preventive control decisions, reducing problem size and computation time while explicitly controlling residual post-contingency severity. Results on the IEEE 60-bus and 118-bus systems, the latter having an approximately seven-times-larger PSCOPF formulation, demonstrate substantial computational reductions with limited impact on the preventive operating solution.

Recent advances in deep learning have significantly improved underwater acoustic vessel recognition. However, existing publicly available datasets are typically limited in scale, hindering large-scale representation learning and downstream task evaluation. To address this limitation, we introduce ONCship, a large-scale underwater acoustic dataset comprising 4521 hours of curated recordings from Ocean Networks Canada (ONC) hydrophones and Automatic Identification System (AIS) data. We evaluate the proposed dataset across three tasks: self-supervised representation learning, vessel-type classification, and MMSI-Level Vessel Classification. Experiments on DeepShip and ShipsEar demonstrate the effectiveness of ONCship for acoustic representation learning, with joint pre-training on ONCship and AudioSet achieving a classification accuracy of 62.45% on DeepShip through linear probing. Furthermore, incorporating ONCship as auxiliary training data improves downstream vessel-type classification performance. Beyond conventional vessel-type classification, we further investigate MMSI-Level Vessel Classification, treating each vessel as a distinct identity. Experimental results show that models fine-tuned with different loss functions achieve Top-1 accuracies exceeding 53% and Top-5 accuracies approaching 69%, demonstrating the feasibility of distinguishing individual vessels through MMSI-Level Vessel Classification. Overall, ONCship provides a valuable resource for underwater acoustic representation learning and vessel recognition, while its rich vessel-level metadata enables further exploration of MMSI-Level Vessel Classification.

Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical model of the world, leaving a sim-to-real gap that can invalidate them for the deployed system. We formulate the runtime monitoring problem for supermartingale certificates, in which a monitor observes a single system trajectory and raises an alarm when there is sufficient statistical evidence that the supermartingale certificate has been violated during execution. We present an algorithm to automatically construct a monitor using betting-based sequential tests that bound the probability of ever raising a false alarm at a user-specified level. Within this betting framework, we formulate strategies that allow the monitor to exploit spatial regularity to improve detection power while preserving this statistical guarantee. We demonstrate the efficacy of our approach on supermartingale certificate benchmarks from the literature.

High-frequency and broadband line-of-sight multiple-input multiple-output (LoS-MIMO) is a key solution for massive space-to-Earth data downlinks. However, in low Earth orbit (LEO) scenarios, it faces spatial rank deficiency from time-frequency coupling correlation and clock deviations from distributed oscillators. This paper establishes a dynamic effective channel throughput (ECT) framework incorporating waterfall characteristics of forward error correction. A hardware-friendly OFDM-based transceiver architecture with subband-adaptive truncation and dynamic timing recovery to achieve robust spatial decoupling is proposed. Through rigorous closed-form derivations, the equivalent signal-to-interference-plus-noise ratio (SINR) under joint errors is quantified, and an optimal scheduling algorithm combining truncation with variable coding and modulation is proposed to maximize ECT. Simulations confirm that the design circumvents outages caused by spatial notches with low complexity, stably enhancing the potential average throughput of a single dual-polarized beam to over 100 Gbps, providing a reliable theoretical foundation and baseband solution for next-generation high-throughput satellites.

Integrated sensing and communication (ISAC) with cloud radio access networks (C-RAN) unifies two foundational technologies for the Internet of Things (IoT), enabling both high-data-rate transmission and accurate environmental awareness. This paper investigates an uplink C-RAN system where multiple remote radio units (RRUs) jointly serve user equipment devices (UEs) and sense a target. We demonstrate that only a subset of RRUs is typically needed to meet sensing requirements, while energy is potentially conserved by deactivating redundant sensing RRUs. Motivated by this, we aim to improve the system's energy efficiency (EE) by jointly optimizing RRU activation, user association, and power allocation, while ensuring localization accuracy through the Cramér-Rao lower bound (CRLB). To address this problem, we propose a model-based iterative algorithm that integrates optimal user association, decision tree-based selection of sensing RRUs, and fixed-point power allocation. This algorithm is further used to train a graph neural network (GNN) framework, ISAC-GNN, to overcome scalability and computational complexity limitations. Our theoretical analysis ensures performance guarantees, while a semi-supervised training strategy enhances stability and generalization. Simulation results demonstrate that ISAC-GNN reduces inference time by 67% relative to the model-based approach, enabling real-time implementation for ISAC system resource management. In addition, ISAC-GNN allows scalable deployment across various system scenarios without retraining the model.

The rapid growth of AI data centers introduces large, workload-dependent loads whose protection and reconnection behavior can affect power-system transient stability. Calibrating dynamic models of such loads is challenging because treating all associated elements as freely estimable may lead to weakly identified or physically confounded parameterizations. This paper proposes an identifiability-guided calibration framework for a composite load model with separate IT, cooling, and auxiliary branches. A nuisance-projected Fisher-information criterion characterizes the local identifiability of the cooling-motor composition, while a workload non-equivalence result shows that voltage-only recalibration cannot reproduce workload-dependent responses. Guided by these results, a grey-box estimator trained with branch-level supervision learns the workload-to-power relation and the cooling composition, while retaining a prescribed UPS protection law and an externally anchored reconnect-ramp duration. The learned workload relation is then distilled into a closed form for transient-stability simulation. Evaluation using 300,000 MIT Supercloud GPU power-utilization samples and 12,600 IEEE 39-bus fault scenarios yields an aggregate active-power $R^2$ of 0.948, compared with 0.856 for an LSTM baseline. Removing branch-level supervision preserves relatively high aggregate accuracy but substantially degrades cooling-component recovery. Under unseen deep voltage sags, the proposed model achieves a transient $R^2$ of 0.750, compared with 0.033 for the LSTM. Closed-loop simulations further show that cooling composition and protection behavior can affect retained-load states and voltage responses near protection thresholds.

Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work, we propose FLARE, a framework for robust visual speech representation learning via cross-modal distillation and joint face-lip modeling. FLARE leverages semantically rich Whisper representations as the acoustic teacher signal, converting them into discrete distillation targets through a random-projection quantizer and pretraining the model with a masked prediction objective. To capture complementary visual information, FLARE employs a dual-stream architecture that jointly processes lip region-of-interest (ROI) and full-face inputs, where the lip stream captures finegrained articulatory motion and the face stream encodes broader facial dynamics. During pretraining, auxiliary audio input is incorporated to facilitate cross-modal alignment, and modality dropout is applied to encourage robust visual-only representations. For downstream lipreading, FLARE serves as a visual encoder coupled with either a lightweight Transformer decoder or an LLM-based decoder. Evaluated on LRS2, LRS3, and the out-of-domain WildVSR benchmark, FLARE achieves strong lipreading performance, with word error rates of 11.7%, 13.2%, and 34.3%, respectively. A progressive design study confirms the contribution of each major component. Further analyses demonstrate that joint face-lip modeling improves robustness under degraded video resolution and large head pose variation, while yielding more phoneme-discriminative visual representations. Code and pretrained models are available at https://github.com/jxzhanggg/FLARE.

In this paper, we investigate a wireless communication network supported by multiple uncrewed aerial vehicles (UAVs). To ensure reliable connectivity, UAVs are desired to approach the boundaries of designated no-fly zones (NFZs) without violating them when mobile users (MUs) enter NFZs. To support heterogeneous communication services, MUs are assigned different priority levels based on their individual communication types. Considering the practical constraints arising from user mobility, heterogeneous communication requirements, and NFZs, the objective is to maximize the weighted sum of MU communication rates, where weights correspond to MU priorities. This is achieved by jointly optimizing trajectory planning, UAV transmission power control, and the binary association factor between UAVs and MUs. To address this challenging optimization problem under dynamic constraints, we develop a soft actor-critic algorithm with prioritized experience replay, which learns an adaptive control policy. Numerical results show that the proposed algorithm outperforms benchmark schemes in terms of convergence speed and stability. Moreover, it effectively meets diverse communication requirements, satisfies UAV energy constraints, and ensures reliable operation under NFZ constraints.

Text-to-speech (TTS) based on masked diffusion models (MDMs) achieves competitive zero-shot speech synthesis performance. Unlike autoregressive (AR) models, MDMs define a corruption process that maps clean speech-token sequences to masked maps and learn a reverse denoising process to recover the reference tokens. However, MDMs commit to intermediate predictions that cannot be revised in subsequent inference steps. Consequently, prediction errors accumulate and propagate, degrading synthesized speech quality. We present Revise-TTS, a single-stage framework that applies hierarchical binary quantization (HBQ) and generative refinement networks (GRN) to zero-shot speech generation. First, we replace finite scalar quantization (FSQ) with HBQ to introduce a scalable binary representation for speech tokens with an explicit coarse-to-fine structure. Second, GRN is trained to reconstruct the token sequence from a mixture of ground-truth and random tokens. During inference, iterative refinement enables self-correction by randomly discarding a small proportion of previously predicted tokens with new predictions. Experiments and ablation studies demonstrate that Revise-TTS achieves competitive zero-shot speech synthesis performance using HBQ. It can further be extended to any MDM-based TTS task as an alternative training and inference method.

Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvements may reflect adaptation to specific question formats, such as multiple-choice questions, rather than genuine advances in audio understanding and reasoning capabilities. To investigate what LALMs actually learn from post-training, we conduct controlled experiments that disentangle improvements in audio understanding from adaptation to specific question formats. Across different models and post-training methods, we find that much of the reported improvement persists even without informative audio, but largely disappears when the question format changes. These findings suggest that post-training often teaches models to exploit task-specific textual cues and answer patterns rather than developing generalizable audio understanding and reasoning capabilities.

Neural instrument synthesizers can clone the timbre of a solo instrument, letting music producers make music with the sound they desire. However, an instrument of interest is often embedded in a fully produced, multi-instrument recording, beyond the reach of existing methods. Source separation before timbre cloning is an approach, but it may suffer from separation artifacts and is also likely a detour for the task. In this work, we propose Point-and-Play, a system that clones the timbre of a cued source directly from a polyphonic mixture and uses it to synthesize new pitches and velocities. To achieve this, we introduce a Cue-Gated Statistics Pooling (CGSP) module that extracts the timbre code of the cued instrument from adapted CLAP embeddings of the music mixture. This extracted timbre code is then used to condition a separately trained note synthesizer together with pitch and velocity inputs. Experiments show that our proposed system outperforms separate-then-clone baselines in terms of timbre fidelity and synthesis quality, achieving comparable performance with that cloned from a clean solo instrument.

In this paper, we develop a Koopman operator framework for modeling and control of traffic flow governed by the Lighthill–Whitham–Richards (LWR) conservation law. We show that the nonlinear dynamics induced by the Greenshields flow–density relation naturally motivate a dictionary of polynomial observables, while the controlled dynamics become bilinear in the lifted Koopman coordinates. A reduced-order Koopman model is identified from spatio-temporal traffic-density snapshots and used as the prediction model in a model predictive control (MPC) framework for ramp metering. The resulting Koopman-MPC is compared with two predictive control baselines that use, respectively, a fixed linearized LWR model and the nonlinear finite-volume LWR model. In a one-hour freeway experiment, the Koopman-based controller maintains the traffic density below the critical Greenshields value and closely reproduces the behavior of the nonlinear PDE-based MPC, with a maximum-density difference below 1%. The Koopman predictor also reduces the mean online model-evaluation cost by approximately one order of magnitude relative to the nonlinear PDE predictor. In contrast, the linearized LWR predictor is computationally cheaper but exhibits substantially larger prediction errors and yields a more conservative metering policy. These results demonstrate that the analytical structure of the nonlinear traffic conservation law can be exploited to construct an interpretable data-driven surrogate for efficient traffic prediction and control