Papers

Fixed linear compression can preserve local Fisher information for a sinusoid yet destroy global frequency identification, while a dark response can make local estimation nonregular. We study both failures for a single complex tone observed through a fixed complex-linear sketch with unknown nonzero complex gain and pre-sketch white Gaussian noise. Near an isolated analytic dark frequency, we separate radial signal vanishing from optimized projective contact between the two signed frequency branches. When the gain magnitude is constrained to a fixed nondegenerate interval, finite contact is equivalent to local quotient identifiability and minimax consistency. The sharp mean-square-error rate is determined by the sum of the radial and contact orders; infinite contact produces exact local aliases. Globally, we formulate a worst-frequency efficientinformation objective on whitened row spaces. We solve it exactly for every even output rank, prove uniqueness and quantitative rigidity of the symmetric-edge projector, and solve the evenaperture co-rank-one case. The rank-two optimum is aliased, and a winding obstruction gives a positive lower bound on the information price of global identification. From three outputs onward, sketches that are globally identifying with an immersive projective response are open and dense and have zero price at the level of suprema; for every even rank of at least four, the exact optimizer has this property. Thus local information preservation, singular recoverability, and global identifiability obey distinct compression laws within one estimation model.

Accurate wireless localization is a key enabler for 6G networks, yet remains challenging under diverse and rapidly changing propagation conditions. Model-based methods degrade when multipath channels are non-resolvable and model mismatches occur, while supervised deep learning demands large labeled datasets and generalizes poorly to new deployments. Inspired by foundation models (FMs) in language and vision, this article presents a unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision. We review the fundamentals of FMs, compare the FM paradigm with existing localization approaches, and introduce a three-stage framework spanning large-scale pretraining, localization-oriented fine-tuning, and context-augmented inference, together with the location-aware applications it enables. Ray-tracing-based case studies show improved positioning accuracy and cross-environment generalization. Finally, we present an outlook on key research directions toward AI-native networks for wireless localization.

Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead. This paper shows that fully digital cell-free mmWave beamforming can be effectively learned from sub-6 GHz CSI using a graph neural network (GNN). Specifically, we represent a CFmMIMO system as a wireless graph, and the GNN is trained to approximate beamformers that maximize the downlink sum-rate based on the available sub-6 GHz CSI. A message-passing mechanism is proposed to capture inter-user interference and inter-base-station cooperation across different network topologies. Simulation results demonstrate that the proposed sub-6 GHz-assisted GNN-based beamformer achieves competitive and often superior sum-rate performance compared to classical baselines that rely on full mmWave CSI.

Flexible sensing arrays are commonly used in underwater acoustic networks, but suppressed by unpredictable geometric deformations. Existing array shape self-calibration methods often estimate individual element positions separately, leading to a high dimensional optimization problem over long arrays. To address this problem, this paper proposes a Bayesian Optimization-assisted Geometry Estimation (BOGE) strategy operating with a hierarchical optimization process and a physics-informed parametric model for array geometry correction. BOGE formulates array shape self-calibration as an optimization problem, where candidate geometries are evaluated by the noise subspace residual. We perform Bayesian optimization to configure the physics-informed parametric model and then refine the selected geometry through numerical optimization. Empirical results show that BOGE achieves lower mean geometric root mean square error (RMSE) than the benchmark methods across a wide range of noise levels. On the public SWellEx-96 dataset, BOGE achieves a geometric RMSE of $0.659$ meters at $166$ Hz. A lake trial further shows that BOGE provides fixed source localization and moving target tracking performance comparable to the comparison methods.

Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction, but strong performance within one cohort may not generalize to a new cohort. We ask whether within-dataset performance remains when the test data come from an entirely held-out rs-fMRI dataset. Each scan is represented as a regularized symmetric positive definite (SPD) correlation connectome, which allows methods to use the geometry of the SPD manifold. We introduce a reproducible age-prediction benchmark across six rs-fMRI datasets: COBRE, ADNIDOD, Cam-CAN, ABIDE, OASIS-3, and ADNI. The benchmark compares a vectorized correlation baseline, Tangent-Space Ridge, SPDNet, and split-wise Riemannian harmonization under within-dataset GroupKFold, pooled GroupKFold, and leave-one-dataset-out (LODO) evaluation. Within-dataset and pooled GroupKFold results are substantially more favorable than LODO results. When an entire dataset is held out, prediction error increases, differences among methods narrow, and performance is strongly affected by age-range mismatch and cohort heterogeneity. The benchmark provides common inputs, model settings, data splits, and analysis scripts so that future SPD matrix learning methods can be evaluated under the same external-validation protocol.

Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split heartbeat structures across token boundaries. We study beat-synchronous tokenization as a physiologically grounded alternative, comparing fixed patches with three beat-aligned strategies: resampled beats, adaptive pooled beats, and resampled beats augmented with R–R interval information. Experiments span two settings: 10-second 12-lead diagnostic classification on PTB-XL after MIMIC-IV-ECG masked pretraining, and 60-second single-lead rhythm classification on Icentia11k after patient-level contrastive pretraining. On PTB-XL, resampled beat tokens achieve the highest mean macro Area Under the ROC Curve (AUROC; 0.8945) and nearly match the best fixed-patch macro Area Under the Precision-Recall Curve (AUPRC; 0.7414), reducing average sequence length from 100 to 11.2 tokens. On Icentia11k, beat-synchronous tokenizers obtain comparable AUPRC to fixed patching with better stability across runs. These results suggest morphology-preserving beat tokenization is a compact, competitive alternative to fixed temporal patching.

Radio access network (RAN) slicing enables ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services to share radio resources, but their requirements create a challenging reliability–throughput tradeoff. URLLC requires low-latency and reliable packet delivery, whereas eMBB targets high sustained throughput. This paper considers downlink URLLC/eMBB RAN slicing and formulates it as a queue-aware long-term eMBB throughput maximization problem subject to URLLC delay-violation, physical resource block (PRB) exclusivity, and slice-budget constraints. To solve this problem, we propose agentic quantum deep reinforcement learning (Agentic-QDRL), a two-time-scale framework that combines agentic slice-level resource control with quantum-enhanced PRB scheduling. At the slow time scale, a perceive–memory–act–reflect (PMAR) controller adapts the resource shares of URLLC and eMBB slices. At the fast time scale, a compact variational quantum circuit (VQC)-based QDRL scheduler performs PRB allocation under the current slice configuration. A feasibility projection and a safety fallback mechanism is further introduced to satisfy scheduling constraints and reduce URLLC deadline violations. Simulation results under different eMBB traffic loads show that Agentic-QDRL improves eMBB throughput, reduces eMBB queue buildup, and maintains URLLC delay reliability compared with classical DRL and heuristic baselines.

Site-specific channel models are essential for wireless digital twins of 6G space–air–ground communication systems. However, 3D maps are difficult to obtain over wide areas, which limits large-area site-specific channel modeling. To address this issue, this paper proposes a remote-sensing-based augmented ray-tracing channel modeling framework. The framework comprises a deterministic RT branch, a measurement-statistical branch, and an RT augmentation branch. To overcome the difficulty of acquiring large-area 3D maps, the deterministic RT branch reconstructs a 3D RT scene from satellite remote-sensing imagery and calibrates its electromagnetic material parameters using measured path loss. To provide the statistical parameters required for RT augmentation, the measurement-statistical branch establishes the marginal distributions and interparameter dependence models of the channel parameters. Specifically, a wideband UAV channel measurement campaign is conducted at 4.60 GHz, and a proposed multipath estimation method estimates the complex amplitudes, delays, and Doppler shifts of the measured multipath. To bridge the gap between RT predictions and measurements, the RT augmentation branch organizes the RT multipath into LoS, LoS-tail, and NLoS components, generates additional short-delay LoS-tail paths, and reallocates the component and path powers according to the measurement-derived statistics while preserving the total RT received power. The validation results show that the proposed framework reduces the path loss RMSE from 5.45 to 4.35 dB and, relative to calibrated RT, decreases the RMS delay spread and normalized Doppler spread RMSEs by 53.03 and 26.48, respectively. The proposed framework provides a site-specific channel modeling approach for 6G space–air–ground digital-twin studies.

Spectrum governance requires rules to be translated into machine-executable decisions while preserving incumbent protection, regulatory authority, and an auditable record of why a decision was made. We present SPECTRA-GOV, a Tri-Layer Adaptive Governance Architecture (TLAGA) connecting international treaty coordination, national adaptive licensing, and real-time enforcement. The work builds on the original reference implementation preserved at v0.1.0-paper and adds a post-audit evaluation layer rather than replacing it. V2 introduces controlled spectral-contention scenarios, corrected geodesic-distance calculation, per-operator selective authorization, paired baseline counterfactuals, policy perturbation, regulatory-change analysis, and causal provenance. Across 10,000 scenarios in each of seven contention classes, incumbent protection was 100.00% in the no-contention control, 99.92-99.87% in weak-to-dynamic classes, 99.47% under strong overlap, and 90.00% in the adversarial close-proximity class. In a paired S3 baseline experiment, selective authorization achieved 100% incumbent protection and 89.43% access opportunity, while the population-wide dynamic baseline achieved 99.91% protection and 99.53% access. Identical results for the dynamic-SAS and SPECTRA-GOV selective variants mean selective admission alone is not claimed as an exclusive algorithmic novelty. The contribution is the integration of policy representation, computational enforcement, auditability, provenance. Enforcement timing was measured in-process, with mean latency increasing from 0.028 ms for one operator to 1.682 ms for 500 operators; these are software benchmarks, not field measurements. The results establish a reproducible computational governance prototype and identify remaining questions before operational or regulatory claims can be made.

This paper studies the problem of model order selection in stationary noise. Single-threshold detection is sensitive to extreme noise excursions, particularly when the detection threshold is lowered to capture weak deterministic components. To augment single-threshold detection, we propose a differential step-down thresholding algorithm. We use a threshold grid in this algorithm. To overcome the threshold grid misalignment error induced by grid evaluation, we utilize randomized grid dithering. Using extreme value theory, we show the clustering of the noise extrema. The stopping rule of the proposed algorithm detects this clustering and stops the algorithm to prevent false alarms. By analytically bounding the threshold grid misalignment error, we prove that our algorithm achieves asymptotic exact order recovery under the 0-1 loss function. Moreover, the proposed algorithm remains robust even if the detection threshold in the original single-threshold algorithm is lowered or extreme noise excursions occur.

We consider a scenario where a legitimate user (Alice) authenticates itself to an authenticator (Bob) by transmitting orthogonal frequency division multiplexing (OFDM) pilots, from which the authenticator extracts the Micro-CSI (M-CSI) fingerprint and compares it against a stored reference via a likelihood test (LT)-based test. We introduce a new spoofing attack, where two adversarial devices collude to first jointly estimate the M-CSI fingerprints of Alice and Bob and then construct a forged signal able to break the authentication mechanism with high probability, limited only by noise effects on the estimates. We derive approximate closed-form distributions of the authentication test statistic under both the legitimate and spoofing hypotheses, enabling the derivation of false alarm and misdetection probabilities in closed-form. We then validate our analytical results against M-CSI fingerprints extracted from experimental data. The results reveal that, given sufficient pilot observations or an equivalent noise statistic between Bob and the attackers, the latter can always drive the test statistics to a random classifier, vanishing the security of M-CSI-based authentication.

This paper proposes knowledge-distilled generative beamforming (KDGen-BF) framework for site-specific multi-user beamforming. KDGen-BF generates a multi-user beamforming weights from low-dimensional reference signal received power (RSRP) observations without acquiring instantaneous channel state information (CSI). To address the ambiguity caused by limited RSRP observations and interference coupling, KDGen-BF formulates multi-user beamforming as a conditional generation problem and directly outputs beamforming weights beyond a finite codebook. A diffusion transformer is trained through knowledge-distillation and exponential-moving-average (KD-EMA) guidance, and multi-candidate strategy is used for online deployment. Numerical results on multiple DeepMIMO scenarios demonstrate that: 1) under limited probing budgets, KDGen-BF outperforms all baselines; 2) with larger probing budgets, KDGen-BF achieves performance comparable to exhaustive search over the discrete Fourier transform (DFT) codebook and outperforms all other baselines; and 3) under noisy RSRP observations, KDGen-BF remains robust and outperforms all compared baselines.

Cross-coupling between the $dq$ coordinates makes the identification of asymmetric grid impedances a challenging problem, particularly near the fundamental frequency where the asymmetric coupling is strongest. Existing schemes usually handle it either by perturbing the two coordinates sequentially, which lengthens the measurement, or by using a time-domain method with a global parametric model whose order must be tuned. This paper develops a single-shot active non-parametric frequency-domain method that avoids both. The equivalent impedance is parameterized by a pair of single-input single-output complex transfer functions. Each spectral line is fitted with a local rational model; the leakage and transient contributions are estimated, so that neither periodic steady-state excitation nor repeated excitation cycles are required. We give the exact finite-time discrete Fourier transform relation for the conjugate-coupled complex-signal model, and analyse the distortion that a stationary-frame filter placed ahead of the Park transform imposes on the identified pair. The method is validated on a controller hardware-in-the-loop platform against an analytically derived small-signal model, for a symmetric grid and for the same grid with an added grid-following converter that renders it asymmetric. Both complex transfer functions and all four real transfer functions of the $dq$ impedance are recovered over a wide band from a single one-second record of a random excitation, at 1 Hz resolution.

The sixth-generation (6G) wireless networks are expected to enable the deep integration of communication, sensing, computing, control, and intelligence in highly dynamic environments. This evolution drives a fundamental transition from conventional passive channel adaptation to proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured. This paper presents a comprehensive overview of this emerging paradigm. We first review channel cognition through the channel knowledge map (CKM) as a systematic framework for learning and exploiting channel characteristics across space, time, and frequency domain. The definitions, construction methods, and applications in wireless networks of CKMs are comprehensively reviewed. Building upon channel cognition, we then review channel reconfiguration technologies from two complementary perspectives: transceiver-side reconfiguration enabled by movable antennas (MAs) and environment-side reconfiguration enabled by intelligent reflecting surfaces (IRSs). For both MA- and IRS-enabled wireless systems, we review their architectures, performance advantages, and key design challenges. Finally, we discuss several promising research directions to inspire further innovations in this burgeoning field.

This paper investigates robust decentralized transmission for cooperative multi-satellite massive multiple-input multiple-output (MIMO) systems under imperfect statistical channel state information (sCSI). In the considered scenario, each satellite has complete access to its local information but receives partial information from other satellites due to limited inter-satellite links (ISLs), with only imperfect sCSI available. To address these challenges, we propose a knowledge distillation (KD) framework that transfers cooperative precoding knowledge from a centralized teacher neural network (NN) to lightweight decentralized student NNs. Specifically, a global-clean teacher, aggregating information from all satellites and accessing accurate sCSI during offline training, transfers its cooperative precoding knowledge to partial-noisy students, relying on complete local information, limited information exchanged by other satellites, and error-corrupted sCSI for local precoding. The teacher NN combines patch-wise self-attention with dual-axis attention to learn inter-user interference and inter-satellite coordination, whereas each student NN adopts a compact per-satellite architecture for efficient onboard inference. The teacher learns a high-quality weighted minimum mean square error precoding policy from global-clean inputs, which is then distilled into the students operating on partial-noisy inputs. To mitigate the resulting teacher-student performance gap, we develop a hybrid KD mechanism with explicit angle- and phase-error calibration. Simulation results demonstrate that the proposed framework significantly enhances the decentralized sum-rate performance and remains robust under diverse configurations.

Teleoperation systems are increasingly used in medical, rehabilitation, and remote manipulation applications, where accurate position/force tracking and stable interaction are essential. In such applications, the remote environment may exhibit viscoelasticity, frictional memory, contact transitions, and other dynamic interaction effects, causing the system response to depend not only on the current state but also on its previous evolution. This history dependence, together with communication delays and uncertain nonlinear dynamics, makes accurate uncertainty compensation particularly challenging. Conventional feedforward neural approximators do not inherently retain temporal information, while fully recurrent architectures may introduce additional computational and online training complexity. To address this limitation, this article introduces the first application of a liquid state machine (LSM) to bilateral teleoperation control. A finite-time adaptive controller is developed using a hybrid position/force auxiliary error system with velocity and force filters, while the LSM is employed to estimate uncertain dynamics by exploiting its intrinsic temporal processing and fading-memory capabilities with a simple adaptation mechanism. Closed-loop stability and finite-time convergence are established through a Lyapunov–Krasovskii framework. Simulations in spring–damper and generalized Maxwell viscoelastic environments demonstrate improved position and force tracking and lower mean execution time compared with an RBFNN-based controller.

This survey provides overview of distributed estimation, filtering, and fault detection techniques in the context of cyber-physical systems (CPS). To establish a strong foundation, we first define essential aspects of linear dynamical systems and related graph theoretic concepts, emphasizing observability conditions that are key for local state estimation. After discussing consensus algorithms, this survey highlights single-time and double-time-scale consensus-based estimation and filtering approaches. We provide a detailed comparative analysis of these methodologies, examining their computational requirements, communication overhead, and performance characteristics in resource-constrained environments. We further explore different diffusion-based estimation techniques and observationally redundant designs to enhance resilience and robustness against failures and adversarial attacks. In addition, we investigate distributed fault detection methods that enable local isolation of faults over large-scale CPS. We present both stateless and stateful detection mechanisms, along with threshold-based techniques that balance detection accuracy and false alarm rates. These approaches are essential for maintaining system integrity and preventing cascading failures in critical infrastructure. This survey concludes with an exploration of diverse real-world applications. We examine implementation challenges and algorithm adaptations in smart grid and power networks, social systems, target tracking and localization, and intelligent transportation systems. By bridging theoretical insights with practical applications, this survey and tutorial provides valuable understanding and research directions in the field of distributed algorithm design for CPS, offering both newcomers and experienced researchers a comprehensive resource for addressing current challenges and future opportunities.

Robust Principal Component Analysis (RPCA) is a fundamental technique for extracting low-rank structures from data corrupted by sparse anomalies and noise. While classical RPCA and its stable variants have been widely studied, their computational cost grows prohibitively with data dimensionality. Motivated by emerging trends in quantum machine learning, this paper introduces Quantum-Stable RPCA, the first quantum algorithm for robust low-rank and sparse decomposition under noisy intermediate-scale quantum (NISQ) constraints. Our approach integrates Quantum Singular Value Thresholding (QSVT) for low-rank recovery with Quantum Sparse Approximation (QSA) for anomaly detection, while explicitly modeling gate, decoherence, and measurement errors inherent in quantum hardware. We establish six theoretical results covering recovery guarantees, identifiability, robustness to approximate structure, quantum noise resilience, convergence of alternating minimization, and generalization bounds, thereby extending classical RPCA theory to the quantum regime. Extensive Qiskit-based simulations confirm that Quantum-Stable RPCA delivers significant runtime improvements over classical solvers while maintaining competitive reconstruction accuracy, even under realistic NISQ noise levels. This work provides a blueprint for quantum-enhanced robust learning, bridging machine learning and signal processing paradigms.

This paper investigates physical-layer security (PLS) over correlated composite generalized-Gamma (GG)/GG fading channels, where both shadowing and small-scale fading follow GG distributions. Using Mellin transforms and Fox-H functions, closed-form expressions are derived for the single-link probability density function (PDF), joint distribution, survival function, and zero-rate secrecy outage probability (SOP)/probability of non-zero secrecy capacity (PNZSC). The general-rate SOP is expressed as an exact double series with one residual onedimensional integral per term. The model includes the Nakagamim/GG and Nakagami-m/Gamma channels as special cases. Numerical results validate the analysis and demonstrate the impact of the fading parameters on secrecy performance.

This paper presents a novel methodology for the estimation of the rotational speed of induction motors operating under non stationary conditions, using acoustic signals acquired by a low cost microphone. The proposed approach integrates multi rate digital signal processing with advanced time frequency analysis to extract speed dependent harmonic components from motor acoustic emissions, while effectively suppressing electrical interference and electromagnetic noise. Numerical simulations and experimental validations are conducted under both steady state and transient operating conditions, considering a wide variety of speed profiles, including slow ramps and abrupt speed variations. The experimental results demonstrate that the proposed method is capable of accurately tracking rapid and dynamic speed changes caused by load disturbances and mechanical irregularities. The estimated instantaneous speed exhibits a high degree of agreement with reference measurements obtained from a conventional speed sensor. The results confirm that the proposed acoustic based technique provides a reliable, noninvasive, and cost effective solution suitable for speed monitoring of induction motors.

The growing demand for high spectral efficiency in 6G and beyond has driven research into adaptive antenna architectures capable of exploiting the spatial structure of multipath propagation channels. Conventional base station arrays are deployed with fixed element positions and cannot adapt to the instantaneous spatial structure of the propagation channel. In contrast, movable antenna (MA) systems enable dynamic reconfiguration of antenna positions, allowing the array geometry to track the channel characteristics of the current user set. While prior MA studies have demonstrated significant gains under perfect or statistical channel state information (CSI) assumptions, the interplay between imperfect instantaneous CSI, antenna placement optimization, and precoder design has received limited attention. This paper addresses this gap by proposing a practical end-to-end framework encompassing uplink pilot transmission, MMSE channel estimation under a clustered multipath model, and downlink ZF precoding designed from estimated CSI. Antenna positions are optimized via particle swarm optimization under two objectives: sum-rate maximization and max-min fairness. Simulation results show that MA gains are most pronounced under sparse, near-LoS propagation and diminish as channel richness increases. Furthermore, we reveal a fundamental coupling between array geometry and precoder design: a fairness-oriented antenna geometry encodes spatial fairness information that is only recoverable when evaluated with a compatible power allocation strategy. A mismatched precoder can completely mask the geometric advantage, leading to misleading conclusions about the robustness of antenna placement to the choice of optimization objective. These findings provide practically relevant guidance for the design and evaluation of movable antenna systems under realistic operating conditions.

A key challenge in fiber-optic integrated sensing and communication (ISAC) is to make the payload-bearing waveform itself serve both functions without a separate sensing waveform or sensing-only silent interval. We propose a continuous discrete multitone (DMT) framework with two waveform modes, in which the same payload-bearing waveform supports forward intensity-modulation/direct-detection (IM/DD) communication and backward distributed acoustic sensing (DAS). A unified phase-sensitive optical time-domain reflectometry ({\phi}-OTDR) model represents distributed Rayleigh backscattering as a finite-memory sensing multipath channel. It shows that conventional pulse-and-wait {\phi}-OTDR requires a round-trip-time-scale silent interval to isolate successive returns, while nonzero off-peak samples in practical matched-filter (MF) pulse compression cause spatial intersymbol interference (ISI). Continuous DMT instead retains superposed returns and separates range-cell contributions through known-waveform channel reconstruction. Cyclic-prefix DMT (CP-DMT) uses a full-memory CP and one-tap frequency-domain equalization (FDE); under sufficient-CP and ideal full-bin inversion conditions, it eliminates MF-correlation-induced spatial ISI. Cyclic-prefix-free DMT (NoCP-DMT) applies regularized least squares (LS) to the long-memory linear convolution, avoiding the CP at higher receiver complexity. Experiments over a 10-km fiber link localize a 600-Hz disturbance applied by a piezoelectric transducer (PZT) near 5.071 km and recover gauge-differential phase with correlations of 0.989 and 0.987 for CP-DMT and NoCP-DMT, respectively. At 2- and 1-V PZT drive levels, the ten-record localization standard deviations are 0.16/0.11 m and 0.18/0.38 m, respectively. The corresponding IM/DD error vector magnitude values range from 4.80% to 5.00%, with no bit errors observed.

Purpose: To develop and evaluate a diffusion-based reconstruction framework for highly accelerated 2D real-time (RT) cine cardiovascular magnetic resonance imaging (CMR). Methods: We trained an unconditional patch-based diffusion model and incorporated it into a reconstruction framework, termed CineDiff, using diffusion posterior sampling for data consistency. CineDiff was evaluated in four settings: (i) 30 retrospectively undersampled breath-held cine at 1.5T and 3T from healthy participants across multiple acceleration rates, (ii) 15 prospectively undersampled free-breathing RT cine at 1.5T and 3T from patients indicated for clinical CMR, and (iii) 10 prospectively undersampled mid-field (0.55T) free-breathing scans, including five from healthy subjects and five from porcine models. For retrospective undersampling, reconstruction quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and deep image structure and texture similarity (DISTS). For prospective undersampling, image quality was evaluated by blinded expert scoring on a 5-point Likert scale. Results: In retrospectively undersampled breath-held cine data, CineDiff achieved higher PSNR and SSIM and lower LPIPS and DISTS than the comparison methods across all evaluated acceleration rates. In prospectively undersampled free-breathing RT cine data, CineDiff received higher expert image-quality scores. Qualitatively, CineDiff reduced block-like artifacts and preserved finer anatomical detail compared with traditional compressed sensing and a variational network method, termed CineVN. Conclusion: CineDiff enabled high-quality reconstruction of highly accelerated 2D RT cine CMR. The method also demonstrated robustness to out-of-distribution data, including mid-field and porcine acquisitions.

High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and existing datasets provide only 2D range-azimuth maps or sparse point clouds rather than raw analog-to-digital converter (ADC) signals. Hardware scaling is expensive, synthetic-aperture scanning is impractical at fleet scale, and learned synthesis methods are bottlenecked by the very data shortage they aim to address. We present mmIR, an open-source differentiable frequency-modulated continuous-wave (FMCW) radar inverse renderer that fits a physics-based forward model to real captures and re-renders from dense virtual apertures to synthesize high-resolution 3D radar data. Because radar resolution is too coarse to recover geometry directly, mmIR performs LiDAR-assisted inverse rendering: using LiDAR-derived meshes as a geometric scaffold, mmIR optimizes per-vertex International Telecommunication Union (ITU) physics materials, vertex normals, and antenna beam patterns through end-to-end automatic differentiation of a phase-coherent multiple-input multiple-output (MIMO) forward model with multi-bounce propagation, polarization, and free-space diffraction. On seven outdoor and six indoor ColoRadar scenes, mmIR achieves 0.914 mean Pearson correlation on range-azimuth maps versus 0.307 for Sionna-RT. Scenes trained on a cascaded imaging radar transfer to a co-located single-chip radar without re-training (0.554 correlation), and dense virtual arrays (100x100 elements) produce single-frame 3D occupancy validated against LiDAR. Project page: https://mmwave-inverse-rendering.github.io/

Cocaine Use Disorder (CUD) is associated with widespread alterations in large-scale functional brain networks, yet the mechanisms contributing to these changes and their relationship to clinical and cognitive outcomes remain poorly understood. To address this gap, we introduce a framework to extract structurally informed dynamic functional connectivity patterns. We then leverage these connectivity patterns to characterize differences in functional brain network organization associated with CUD and to examine their relationship with clinical measures. Specifically, we applied Laplacian spectral smoothing to each participant's functional connectivity matrix using individualized structural priors derived from diffusion imaging. These structurally informed connectivity features were subsequently used to examine cross-network interactions and characterize dynamic community organization across functional brain states. Our findings indicate that individuals with cocaine use disorder exhibit increased integration and recruitment accompanied by reduced flexibility in the functional brain networks, with the most pronounced alterations in visual, attentional, and control systems. In addition, structurally informed functional connectivity features were predictive of weekly cocaine use within the CUD cohort. Overall, these results highlight the value of structurally informed dynamic connectivity measures for characterizing network-level alterations associated with cocaine addiction and for linking these alterations to clinically meaningful measures of cocaine use severity.

Rapid inter-satellite traffic growth in LEO constellations demands spectrally efficient, low-latency optical relaying. While amplify and forward (AF) relays are latency-efficient, they propagate noise; conversely, decode and forward (DF) relays suppress noise but incur significant complexity via O/E/O conversion. This paper proposes an all-optical regenerative relay for M-ary pulse amplitude modulation (M-PAM) multi-hop links under pointing errors. A parallel optical hard-limiter (OHL) bank performs symbol-level discrimination, regenerating signal levels directly in the optical domain. A variable gain EDFA is employed to stabilize power and define the threshold-stable region. By incorporating pointing-induced fading, various noise sources including ASE-ASE and signal-ASE beat noise and implementation-dependent decision noise, we derive closed-form per-hop symbol error rate (SER) expressions. These are extended to end-to-end performance using a Markov transition-matrix model for arbitrary modulation order and hop count. Analysis of beamwidth, pointing accuracy, and threshold scaling demonstrates reliable multi-hop operation, avoiding both AF noise accumulation and DF O/E/O processing overhead. Verified by Monte Carlo simulations and numerical integration, this framework provides a design benchmark for low-latency, pointing-aware all-optical regenerative relaying.

Compact ultrasound imaging systems are increasingly constrained by receiver-side sampling, conversion, memory, and data-transfer requirements. This work presents a compressed-sensing pipelined successive-approximation-register analog-to-digital converter (CS-SAR ADC) for acquisition-side temporal compression of pre-beamformed medical ultrasound radio-frequency (RF) data. Pseudo-random polarity modulation and charge-domain accumulation are embedded in the SAR sampling network so that multiple consecutive RF samples are encoded into one measurement before quantization, supporting temporal compression ratios of $N_{cT}=1$, 2, and 4. The compressed outputs are recovered off chip using a probe-specific pulse-dictionary RF model and then processed with conventional ultrasound beamforming. A 65-nm CMOS prototype was measured with a 1.2-V supply and 50-MHz master clock. In the non-compressed mode, it operates at 10 MS/s, consumes 964.49~$\mu$W, and achieves 44.12-dB SNDR and 56.40-dB SFDR for a 7.7-kHz input. The ADC output rates decrease to 5 MS/s and 2.5 MS/s for $N_{cT}=2$ and 4. Across all evaluated RF traces, median NCC values were 0.981 and 0.932, with median NRMSE values of 0.36 and 0.56, respectively. Wire-phantom localization error remained below 0.04 mm with no appreciable FWHM degradation. In the speckle-rich cyst phantom, SSIM remained 0.94 and 0.87, while CNR decreased from 3.534 in the reference to 2.047 and 1.379. These results demonstrate a hardware-realistic tradeoff in which temporal compression substantially reduces ADC conversion count and output data rate while preserving point-target geometry, whereas low-contrast cyst conspicuity is more compression-sensitive.

We describe our submission to Task A of the 1st DAFx parameter estimation challenge. The task is to recover the six physical parameters of a simulated metal-plate reverberator – its dimensions and material properties – from a single impulse response (IR). We treat this as a black-box optimization: candidate parameter sets are fed to the simulator and scored by a loss against the target IR. The method has two stages. The first uses CMA-ES, an evolutionary optimizer, to recover five of the six parameters, comparing IRs under an amplitude-normalized loss. Amplitude normalization makes the search robust but discards the cue to the sixth parameter, the plate's surface density; a second stage therefore estimates it alone, with a ternary search on the un-normalized loss. As the choice of loss strongly affects the search, we select it beforehand, and analyze why compression in the common multi-scale spectral loss degrades recovery. Finally, we test our method on a validation set of 50 IRs, discuss a pathological failure mode, and ablate to justify having two different stages instead of a unified CMA-ES search.

This paper develops the first distributed cross-layer algorithm for joint congestion control, routing, scheduling, and power control in covert multi-hop, multi-modal wireless networks, where adversarial wardens (Willies) monitor radio modalities via energy detection. The Detection Error Probability (DEP), the probability that a Willie fails to reliably detect ongoing transmissions, is generally non-concave in the transmit powers, making DEP-based covert network optimization challenging. We resolve this by constructing the tightest concave lower bound on the log-DEP, yielding a conservative convex problem that guarantees satisfaction of the original DEP constraints and unifies hard covertness constraints and covertness-utility maximization in a single problem. We develop a Parallel Proximal Alternating Direction Method of Multipliers (PP-ADMM) algorithm for the resulting cross-layer problem and prove global Q-linear convergence, i.e., exponentially fast convergence, to the set of optimal solutions under standard regularity conditions. Numerical results confirm linear convergence and demonstrate robust tracking performance under channel fading and Willie mobility.

In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.