Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows. While machine learning surrogate models accelerate the simulation step, optimizing designs still requires utilizing iterative black-box search methods. This iterative nature scales poorly, making multi-corner sweeps computationally expensive. As a solution, this paper proposes amortized neural optimization (ANO) for pre-layout SI design. ANO entirely eliminates iterative black-box inference by utilizing fully differentiable neural network surrogate models. ANO extracts analytical gradients from the surrogate to train a global optimization policy. Instead of solving the optimization problem repeatedly at inference, the optimization process is learned offline and therefore amortized. Once the ANO policy is trained, it maps different channel contexts directly to near-optimal design parameters in a single deterministic forward pass. The efficiency and accuracy of the ANO framework are demonstrated based on three complex SI design scenarios, including DDR5 decision feedback equalization (DFE), 9-dimensional SerDes Tx/Rx co-equalization, and DDR3 DQS differential pair routing to optimize eye diagram metrics under intra-pair skew constraints. By trading roughly 10% in optimality compared to instance-specific black-box algorithms, it realizes speedups of three to four orders of magnitude. For a large-scale 320,000-instance multi-corner SerDes sweep optimization, ANO collapses what would have taken days of computation using iterative search algorithms into a single batched forward pass that completes in milliseconds. This transforms computationally expensive SI optimization into real-time and interactive pre-layout DSE.

Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire. This study aims to generate synthetic MRI data exhibiting FCD, assess their realism, and evaluate their impact on automated FCD detection, particularly in reducing the need for manual annotations. Methods: T1-weighted (T1w) and T2-weighted Fluid-Attenuated Inversion Recovery (FLAIR) MRI scans from 131 FCD patients and 90 healthy controls from multiple (3) sites were retrospectively studied. Synthetic MRIs were generated by conditioning a generative network on binary FCD masks. Two neuroradiologists identified real images from a random set of 14 real and 14 synthetic scans. Three nnU-Net models were trained to detect FCD using: (i) real-only (35 FCD / 35 controls), (ii) real (35 FCD / 35 controls) plus synthetic augmentation, and (iii) expanded real data (70 FCD / 70 controls). Results: Experts showed limited ability to distinguish real from synthetic images, with classification accuracy of 60% for T1w and 70% for FLAIR (inter-rater agreement kappa = 0.86). Augmenting automated FCD detection with synthetic data increased sensitivity by 8.14% (p = 0.12) and improved model confidence at true lesion sites (0.83 +/- 0.11 to 0.89 +/- 0.12; p = 0.02). The expanded real-data model further improved sensitivity to 73.8% (p < 0.001) and confidence to 0.90 +/- 0.14 (p = 0.01). Conclusion: Conditional generative networks can generate realistic synthetic FCD-MRIs, reducing labeled data needs by approximately 20% while maintaining equivalent sensitivity. Equivalent amounts of real data, when available, remain more effective than synthetic augmentation.

In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is trained using coherence maps derived from precisely coregistered Sentinel-1 SLC data to learn the relationship between backscatter magnitudes and coherence. The model is trained on 12-day SLC pairs and evaluated across different datasets, including coregistered SLC products and open access analysis-ready data, covering diverse radiometric properties, geometries, and locations. Experimental results demonstrate that the proposed method achieves high-resolution coherence regression with improved accuracy compared to existing intensity-based approaches. The network generalizes well across diverse geographical locations and even across different temporal baselines that were never seen at training time. Additionally, the ability to operate on globally available analysis-ready data, such as ground range detected data, e.g., distributed through Google Earth Engine, enables its large-scale application in mission design, change monitoring, and diverse mapping tasks.

This paper presents a low-complexity motion detection method for outdoor Wi-Fi sensing based on a model-driven approach. The method exploits the structural characteristics of the phase components in channel state information (CSI) for low-multipath propagation environments, which are generally considered disadvantageous for Wi-Fi sensing, to mitigate the phase offset errors originating from wireless devices. In addition, phase averaging provides a processing gain that reduces the random noise components, including quantization and thermal noise. The theoretical basis of the method is described and its effectiveness is experimentally evaluated using Compressed Beamforming frames obtained from commercial IEEE 802.11ac devices. The experiments primarily focus wild crows flying in an outdoor orchard environment. The experimental results demonstrate that the method can detect birds even when they fly several meters away from the direct line-of-sight path between the transmitter and receiver antennas. Furthermore, the results indicated that fluctuations caused by vegetation movement were negligible when the wind speed was less than 3~m/s. The proposed approach is expected to be applicable not only to orchard monitoring but also to other outdoor Wi-Fi sensing applications in low-multipath environments.

Integrated sensing and communication (ISAC) has emerged as a key technology for 6G systems. To support the development of ISAC systems, accurate channel modeling and simulation for performance evaluation is essential. Recently, 3GPP introduced a standardized ISAC channel model and its associated calibration procedure for this purpose. However, due to the complexity of the modeling methodology and the lack of fully explicit implementation details in the 3GPP reports, different implementations may lead to inconsistent or unsynchronized simulation results. To address this issue, in this work, we implement the 3GPP ISAC channel model simulator specified in TR 38.901 and conduct a comprehensive calibration analysis. We compare the simulation results with the reference results reported by companies in 3GPP and discuss several key implementation details to provide insights into the implementation and calibration of the simulator. To facilitate reproducibility and further research, the developed simulator, together with the relevant datasets and calibration results, has been released as an open-source project on GitHub.

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchical UAV networks under hardware impairments (HWI) and imperfect channel state information (ICSI). This paper proposes a hierarchical ad hoc UAV network with non-linear EH and RSMA to enhance both energy and cost efficiency, where UAVs harvest energy from surrounding UAVs. For a practical scenario, we consider the effect of HWI and ICSI in our proposed system. To the best of the authors knowledge, this study is the first to investigate such a scenario in the literature. The outage probability expressions for ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-$m$ fading channels while considering practical constraints such as HWI, ICSI, and non-linear EH. Additionally, approximate outage probability expressions are derived for high transmit power regimes. Subsequently, we formulate two optimization problems to enhance reliability and performance. Our findings indicate that the proposed system outperforms all benchmarks in terms of outage probability.

Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or captioning. We present VISA, our submission to the Interspeech 2026 Audio Reasoning Challenge (Agent Track), evaluated via the MMAR Rubrics for correctness and reasoning quality. Under a "LALM as a Tool" paradigm, VISA strengthens large audio language models with auxiliary multi-modal evidence while avoiding heavy orchestration. The system integrates three components: multi-modal feature extraction for complementary audio and acoustic-visual clues, model-voting inference with consistency checking for stable predictions, and fine-grained category-aware routing to resolve disagreements and select rubric-aligned reasoning chains. On the official Agent Track leaderboard, VISA ranks 2nd overall with a 66.23% Rubrics score. It also achieves 77.40% Accuracy, the highest among all systems listed across both the Single Model and Agent tracks.

Current Audio-Visual Speech Recognition (AVSR) models achieve near-perfect performance on the standard LRS3 benchmark, raising concerns of adaptive overfitting. To systematically assess true generalisability, we construct a highly controlled, unseen evaluation set subsampled from the massive MultiVSR dataset. Unlike standard out-of-distribution benchmarks, our subset strictly matches the acoustic, visual, and demographic distributions of the LRS3 test set. Evaluating five state-of-the-art architectures reveals a universal performance collapse, proving that current systems fail to generalise even under strictly aligned conditions. Through a fine-grained attribute analysis across seven factors, we isolate the specific drivers of this degradation. Furthermore, we uncover a profound lexical bias, expose distinct error patterns, and surprisingly reveal that audio-visual performance even lags behind audio-only settings. We release our matched test set for future benchmarking.

Video-to-audio generation has made significant progress in achieving semantic consistency and temporal alignment from silent videos. However, audio contains rich stylistic attributes such as timbre and tempo that are difficult to infer from visual and textual inputs alone. While reference audio can serve as additional conditioning, it is typically treated as a holistic signal, limiting fine-grained style control. We propose AudioIM, an attribute-aware framework that explicitly models timbre and tempo as separate control factors rather than relying on holistic prompt conditioning. Dual encoders extract complementary timbre-related and tempo-related representations, which are injected through global conditioning. A masking-based training strategy enables effective latent prompt conditioning at inference. Experiments on VGGSound show improved style similarity while preserving semantic alignment and synchronization. Audio samples are available at: https://anonymousdemo757.github.io/.

In this letter, we investigate robust beamforming design for a movable antenna (MA)-enhanced secure integrated sensing and communications (ISAC) system with imperfect eaves?dropping channel state information (CSI). To improve radar sensing performance, we formulate a radar signal-to-interference?plus-noise ratio (SINR) maximization problem by jointly opti?mizing the transmit beamforming and antenna placement while ensuring communication data security. However, the resulting op?timization problem is inherently intractable due to the nonlinea mapping from antenna positions to channel coefficients, as well as the eavesdropper (Eve) channel uncertainty. To handle these challenges, we propose a block coordinate descent (BCD)-based algorithm incorporating successive convex approximation (SCA) and fractional programming (FP) techniques. Simulation results show that our proposed algorithm exhibits fast convergence and achieves a significant improvement in the radar SINR while guaranteeing communication security.

Integrated sensing and communication (ISAC) enables sensing and communication (S&C) functionalities to share spectrum, hardware, and signal-processing resources, but the resulting inter-functionality interference creates a fundamental receiver-design challenge, particularly in uplink operation. This paper develops a rate-splitting (RS)-inspired framework for uplink near-field ISAC. The framework generalizes the sensing-centric (S-C) and communication-centric (C-C) endpoint orders of non-orthogonal multiple access (NOMA)-inspired ISAC by splitting the communication message across the sensing operation. Closed-form expressions are derived for the communication-rate (CR) and sensing-rate (SR), accounting for residual sensing interference from target-response estimation uncertainty. The achievable CR-SR rate region is characterized under sensing-matched illumination, where the proposed single-frame RS-inspired boundary contains the NOMA-inspired time-sharing region. Unlike the classical Gaussian uplink multiple access channel, where RS recovers the time-sharing dominant face, the split factor in uplink ISAC also reshapes the sensing-stage interference, allowing the RS-inspired boundary to match or strictly enlarge the S&C tradeoff. High-SNR analysis shows that, for non-aligned S&C channels, residual sensing interference changes the rate offsets but not the leading S&C slopes, whereas in the fully-aligned case it becomes slope-limiting. Using an aperture-aware near-field channel model, large-array limits are derived, showing that achievable rates remain finite as the array grows. Numerical results validate the analysis and demonstrate the benefits of the RS-inspired scheme, the impact of residual sensing interference, and the bounded large-array behaviour induced by physically consistent near-field modelling.

Dynamic Facial Expression Recognition (DFER) is a key enabling technology in affective computing, human-computer interaction, and intelligent multimedia systems. Despite the significant influence of cultural nuances on FER performance, most existing FER systems assume that emotional expressions are universally consistent across populations. This variation can be attributed to systematic differences in facial muscle activation patterns across cultures. A major challenge in advancing cross-cultural FER lies in the scarcity of culturally diverse benchmark datasets. To address this, a new hybrid multicultural video dataset termed Global Cross-Cultural Facial Expression Recognition (GCC-FER) is introduced. GCC-FER comprises 23,934 video samples spanning four cultural groups (African, Caucasian, East Asian, and South Asian) across seven basic expressions, combining psychologically supervised in-house data collection for underrepresented populations with rigorous ethnicity filtering of existing sources. To the best of our knowledge, GCC-FER is the first large-scale global cross-cultural DFER dataset designed to address these demographic gaps. Leveraging this dataset, behaviorally grounded cultural priors are derived for each cultural group and a global prior for practical deployment. A Culture-Aware FER (CA-FER) system is proposed to mitigate cultural bias by adaptively recalibrating latent facial representations. Extensive experiments on GCC-FER and DFEW demonstrate that the proposed system consistently improves FER performance across multicultural settings.

Reliable inspection of nanosurfaces is essential to ensure the quality of nanostructure manufacturing. Angle-resolved scatterometry provides a non-invasive inspection method that can be used in-line but often suffers from long acquisition times due to dense angular sampling. This paper addresses the data acquisition challenge by proposing an end-to-end compressed learning framework for 5-level vacancy deficiency detection in zinc oxide nanograss using ARS images. The proposed framework integrates a learnable latitude-based sampling layer with a convolutional neural network, allowing sampling and classification to be jointly optimized during training. The sampling layer exploits the physical structure of ARS patterns and learns informative latitudinal regions, which reduces the sampling search space and improves convergence. Evaluation results show that the proposed approach achieves high and stable deficiency-level classification performance under different noise conditions. Using full ARS images, the model achieves 94.2% accuracy for five-level deficiency classification and 98.6% accuracy for separating deficient from non-deficient nanosurfaces. The proposed sampling model matches full-image performance while using up to 90% fewer angular sampling points. Even when sampling points are reduced by 99.7%, the classification accuracy decreases by less than 10 percentage points. To further improve training with limited data, we also studied a GAN-based augmentation approach and used GAN-generated data for model pretraining. Augmented data resulted in fast convergence within only a few fine-tuning epochs.

Low-altitude wireless networks (LAWN) envision a reconfigurable 3D network capable of supporting mission-critical aerial operations. This paper presents a reconfigurable intelligent surface (RIS)-assisted LAWN to establish a reliable communication with an unmanned aerial vehicle (UAV) across varying wireless channel conditions and signal blockages. A low complexity stripe-based RIS phase shift optimization framework is proposed to simultaneously enhance communication reliability and provide passive sensing capability for UAV tracking under 3D mobility. Unlike high-complexity optimization approaches, the proposed method leverages the inherent structural phase-gradient of the RIS adjacent elements to significantly reduce the search space for calculating and updating the RIS configuration as the UAV moves. The analysis and simulation results demonstrate that the proposed framework outperforms conventional benchmarks in convergence speed and computational efficiency, while maintaining robust, high signal-to-noise-ratio (SNR) connectivity even in the presence of phase estimation errors and low SNR regimes. In addition, the measurement experiments using a real RIS prototype in an outdoor campus environment are performed to demonstrate the practical viability of the proposed approach.

Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution. We present DaX, a pathology vision foundation model that adapts DINOv3-style self-supervised learning to whole-slide histopathology. DaX is initialized from natural-image DINOv3 weights and incorporates continuous magnification training, cross-scale tissue views, orientation-agnostic and acquisition-robust augmentation, multi-input-size training, and Gram-anchored dense consistency. These designs aim to connect local cellular morphology with global tissue architecture while stabilizing dense token-level representations across input scales. We further construct a WSI-level benchmark comprising 161 clinically meaningful tasks from 44 public datasets, covering 28,182 patients and 34,394 slides across four clinical domains and nine task categories. All models are evaluated under a fixed patient-level cross-validation protocol with fold-level statistical ranking, enabling reproducible comparisons that are less sensitive to split-dependent variation. Across this benchmark, DaX achieves the highest mean performance across tasks and consistently strong task-level ranking scores, with gains spanning diagnostic pathology, biomarker and molecular profiling, tissue/specimen context, and risk, response, and prognosis. These results support DaX as a transferable visual encoder for computational pathology and provide a standardized evaluation framework for future pathology foundation models. Project page: https://alibaba-damo-academy.github.io/DaX/benchboard/.

Speech bandwidth extension (BWE) aims to reconstruct high-fidelity wideband audio from narrowband inputs. While recent approaches have made significant progress, they often struggle to reconstruct realistic high-frequency phase and harmonic structures, leading to perceptual artifacts. In this paper, we propose FSC-Net (Full-Spectrum Context Network), a parameter-efficient architecture designed to explicitly model cross-band harmonic dependencies. By integrating Fast Fourier Convolutions (FFCs) into a complex spectral mapping framework, FSC-Net expands its receptive field to the entire spectrum, capturing long-range frequency interactions effectively. To address the ill-posed nature of high-frequency generation, our novel frequency-progressive learning curriculum guides the network to reconstruct spectral details from coarse to fine. Experimental results on the VCTK and unseen EARS datasets demonstrate that FSC-Net delivers consistently strong reconstruction quality and generalization, particularly in the challenging VCTK 4 kHz-to-48 kHz task. Compared to scaled-up baselines, our model attains leading LSD and PESQ scores while maintaining a highly compact parameter footprint (1.54 M).

Driven by the emerging low-altitude economy, uncrewed aerial vehicle (UAV) swarms offer flexible integrated air-ground access and backhaul. However, providing seamless connectivity is difficult due to the interdependent dynamics of user mobility and building blockages in these 3D scenarios. These factors create rapidly shifting bottlenecks in end-to-end paths. Furthermore, the multi-dimensional nature of joint control limits the effectiveness of traditional heuristics. To address these challenges, a \textbf{\underline{M}}ulti-Scale \textbf{\underline{R}}adio \textbf{\underline{M}}ap-\textbf{\underline{G}}uided (MRMG) framework is proposed. The MRMG framework handles heterogeneous dynamics by integrating three distinct levels of radio information: global-level maps provide regional coverage insights, local-level maps capture neighborhood-scale service conditions, and link-level maps characterize high-resolution channel features. This design effectively decouples macro-movement from micro-link adaptation. To yield long-term performance improvements, A multi-agent reinforcement learning (MARL) controller learns cooperative policies for UAV movement, next-hop selection, and transmit-power control. Simulation results show that the MRMG framework not only improves network throughput but also significantly bolsters cell-edge service, nearly doubling the 5th-percentile user rate.

The extended Phaseless Rytov Approximation (xPRA) is a recently proposed device-free RF imaging technique that provides high-resolution reconstructions of the imaging region using only phaseless measurements, such as received signal strength (RSS). Because of its phaseless formulation, it can be implemented straightforwardly using existing wireless commu?nication infrastructure. It also outperforms well-known device?free phaseless RF imaging methods such as Radio Tomographic Imaging (RTI). The linear phaseless formulation used in xPRA(and RTI) makes these methods potentially useful for integrated sensing and communication (ISAC) systems in next generation wireless networks since they do not require wide bandwidths. However, so far, both xPRA and RTI have primarily been formulated in two dimensions (2D). This paper introduces a 3D extension of xPRA, which we call the extended three-dimensional phaseless Rytov approximation (x3DPRA). The novelty of our approach is that it preserves the straightforward implementation advantages of RTI and xPRA while enabling volumetric (3D) imaging. Simulation results show that x3DPRA provides good estimates of location and shape and can also reconstruct object material attenuation. We present the 3D formulation, validate it with a 2D model comparison, and report simulation results demonstrating its performance.

Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data. However, the scarcity of high-quality annotated audio data remains a fundamental bottleneck for scaling. Through probing signal detectability analysis, we identify fine-grained spectrotemporal perceptual weaknesses in a foundation LALM. To address these challenges, we propose Spectrotemporal Counting (SpectCount), a data-efficient fine-tuning approach based on fully synthetic audio signals generated on-the-fly, without relying on real-world audio, annotations, or pretrained generative models. SpectCount not only resolves the observed weaknesses but also improves performance on diverse auditory benchmarks spanning sound, music, and speech, unseen during fine-tuning. These results suggest that weakness-targeted synthetic signals provide a data-efficient path toward enhanced auditory understanding capabilities in LALMs.

Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability. In SAR target interpretation, electromagnetic scattering information provides a physically grounded cue beyond visual texture and has been widely exploited for target interpretation. However, existing methods remain dominated by local scattering center representations. Such unordered and component-agnostic representations are highly unstable for aircraft targets. As a result, physically existing components with weak scattering responses are often missed, resulting in the incomplete reconstructed topology structure. To address this limitation, we establish Semantic Scattering Structure Understanding as a new paradigm for SAR aircraft interpretation. Semantic scattering keypoints are defined to associate local electromagnetic responses with physically meaningful aircraft components, while visibility-aware attributes are introduced to retain weakly observable yet physically existed components. The keypoints are further organized into a stable semantic scattering structure. Build upon this, we propose S3U-SAR, a physics-driven framework to localize semantic scattering keypoints and construct the complete representation constrained by multi-dimensional physical priors containing scattering heterogeneity, rigid-body topology, speckle uncertainty. A confidence-gated joint supervision strategy is further introduced to alleviate optimization conflicts. We construct KP-SAR-Aircraft-1.0, the first fine-grained benchmark for semantic scattering structure understanding. Extensive experiments demonstrate that S3U-SAR achieves the best performance compared with baselines. Cross-category and cross-dataset evaluations further verify its robustness and transferability.

This letter studies variable-length finite-rate CSI feedback from a structural perspective and proposes CsiCoGen, a novel generative feedback structure with a transferable codebook mechanism without joint training. The UE maps $H_0$ into an ordered sequence of codebook indices, while the BS recursively recovers CSI from any received partial sequence of feedback indices using a shared denoising prior. This enables flexible control of feedback sequence length and per-step quantization precision through codebook size. CsiCoGen does not require jointly training a task-specific feedback encoder or codebook with the reconstructor, and the same online structure can be paired with different pretrained denoisers. In this work, we instantiate the decoder with a generative diffusion model. Simulation results on COST2100 show favorable rate-NMSE and rate-$ρ$ tradeoffs against representative baselines, with CsiCoGen reaching about -31 dB indoor NMSE and -20 dB outdoor NMSE in the high-rate regime while demonstrating scalable decoding complexity and adjustable per-step quantization precision.

Scripted vs spontaneous speech detection is appealing for interview guardrails, but benchmark performance can be inflated by shortcuts tied to corpus identity, channel conditions, and recording artifacts rather than speaking style itself. We present SEAM, a shortcut-aware framework for real-time scriptedness detection that combines uniform preprocessing, seam-aware sampling, non-speech augmentation, and a compact DistilHuBERT backbone. With 8s windows, the model achieves 0.971 +- 0.004 ROC-AUC on an external interview-domain evaluation set. Removing the shortcut-prevention components improves internal held-out metrics but sharply reduces external performance, indicating shortcut learning. Post-training quantization reduces the model footprint to 41.8MB with little loss in external performance. The results demonstrate that robust real-time scriptedness detection depends not only on the backbone, but on shortcut-aware data design and evaluation. We release code and model checkpoints.

We present BiEAR, a human auditory-inspired adaptive binaural front-end for multi-speaker localisation and distance estimation. Inspired by medial olivocochlear (MOC) feedback in human hearing, BiEAR uses a neural controller to adaptively adjust the frequency selectivity of a binaural auditory filterbank during inference. This yields time-frequency adaptive representations for ears, enabling the model to respond to changing acoustic conditions. We evaluate BiEAR on multi-speaker localisation and distance estimation in anechoic and real-room environments. Results show that the adaptive front-end improves localisation accuracy and robustness to unseen speakers and rooms compared with commonly used fixed binaural front-ends. Visualisation and analysis of learned filter adaptations show that BiEAR emphasises informative frequency bands over time. These findings suggest that adaptive, biologically inspired binaural front-ends can improve machine hearing robustness in complex acoustic scenes.

Copula functions have been widely employed in wireless communication analysis to model dependence structures and evaluate system performance. However, existing studies generally express performance metrics in terms of copula dependence parameters without explicitly characterizing their admissible regions. This letter introduces the concept of copula dependence parameter regions and investigates its significance in wireless communications. Considering a two-user wireless multiple access channel (MAC) with correlated Rayleigh fading modeled by the bivariate Farlie--Gumbel--Morgenstern (FGM) copula, explicit parameter regions are derived from communication-theoretic and probabilistic perspectives using outage probability and Pearson correlation coefficient (PCC) constraints. The results show that practical communication and statistical requirements can significantly shrink the classical copula admissible interval, rendering some theoretically admissible dependence structures infeasible. Numerical examples illustrate the proposed concept and its practical implications.

Efficient sparse array reconfigurability is essential for cognitive sensing in dynamic radio frequency environments, where rapid interference variations require both adaptability and stability. This work presents a framework for designing sparse arrays optimized over broad angular sectors, enabling near-optimal beamforming that maximizes the signal-to-interference-plus-noise ratio (SINR) across a range of interferer angles. Full data correlation matrices are computed for candidate configurations, and an angular-sector-based class reduction strategy is applied to merge adjacent sectors dominated by the same configuration, resulting in 56 representative classes. Controlled up- and down-sampling produce four dataset variants involving, high and low sample count, balanced and unbalanced datasets, to systematically evaluate the effects of dataset size and class distribution on neural network performance. A lightweight convolutional neural network (CNN) and a deeper ResNet 50 architecture are trained and evaluated using these datasets. Results demonstrate high classification accuracy, with ResNet 50 achieving up to 97.3%, while SINR deviations remain below 1% for most classes and below 5% even for challenging interference angles near broadside. The proposed approach enables robust sparse array selection, maintains strong SINR performance, reduces unnecessary reconfigurations, and provides an effective framework for real-time cognitive sensing and adaptive interference mitigation.

Myocardial perfusion quantification using contrast-enhanced ultrasound offers a bedside non-ionizing alternative to nuclear imaging modalities. However, its clinical adoption is hindered by time-consuming manual labelling. Automated segmentation has proved challenging due to a paucity of in-domain training data. Adapting strategies currently used to optimise large language models for large datasets, we apply neural scaling laws to predict network performance for myocardial segmentation. We extrapolate performance on subsets of the data to determine optimal network size on the CAMUS echocardiography dataset and a 25-patient contrast-enhanced ultrasound (CEUS) dataset. Finally, we validate the clinical utility of our models by comparing the final myocardial perfusion parameters with those obtained by a senior cardiologist. Extrapolation based on the scaling law is predictive of test loss at the full dataset size, allowing us to select two networks that obtained state-of-the-art performance on CAMUS with a 240-fold reduction in parameter count. We observe the gradient of the scaling law transfers from CAMUS to the CEUS dataset with a bias in the predicted losses. The automatically segmented masks perform equivalently to a senior cardiologist in myocardial perfusion quantification. These results establish neural scaling laws as a practical tool for data-driven compute-optimal model design for small imaging datasets.

This paper investigates the use of convolutional neural networks (CNNs) for learning sparse array configurations that achieve near-optimal beamforming under varying source and interference angles. Unlike conventional or convex optimization based algorithms, the proposed deep learning approach enables rapid reconfiguration of sparse arrays in highly dynamic propagation environments. The paper considers a single desired source and a single interference signal at arbitrary angles, analyzing scenarios with both fixed and varying desired source directions. To avoid retraining for each possible source angle, an array pre-steering strategy is introduced, whereby the network is trained only at broadside, while test inputs are pre-steered to align with the broadside direction. To account for practical imperfections, the effect of pre-steering errors is examined, and a robust error-augmented training is adopted. The approach systematically incorporates small, structured pre-steering perturbations during training, enabling the network to maintain high classification accuracy and maximize the signal-to-interference-plus-noise ratio (SINR) even under angular uncertainty. The results demonstrate that the proposed method achieves over 90% test accuracy across wide ranges of source and interference angles, highlighting its potential for real-time, robust sparse array configuration in dynamic environments.

This work conceives two affine precoding based system models, common precoding with joint channel estimation (CP-JCE) and user-specific precoding for decoupled channel estimation (USPDCE). Considering a dual-wideband effected partially connected architecture, we rigorously model the terahertz (THz) multiple input multiple output (MIMO) channel for each subarray corresponding to each user by incorporating the absorption, reflection, and freespace losses. Next, to address the significant bandwidth overhead associated with conventional pilot-based channel estimation, we employ superimposed pilots. Building on this, we formulate a structured sparse channel model and develop a variational Bayesian inference algorithm that jointly estimates the channel coefficients and learns the underlying sparsity structure through hyperparameter inference, thereby enabling robust and high-precision superimposed pilotbased channel estimation under severe model uncertainty. Lastly, we compare our results for both systems and provide a trade-off analysis between them.

This work conceives SEMIKHORN, a semisupervised channel charting (CC) framework for mmWave localization, which leverages t-SNEkhorn, a doubly stochastic variant of t-distributed Stochastic Neighbor Embedding (t-SNE) that utilizes entropic optimal transport to construct pairwise similarities. Unlike standard t-SNE, which normalizes affinities independently for each data point, t-SNEkhorn generates globally balanced similarities ensuring consistent neighborhood representation. We consider wireless networks with distributed base stations (BSs) equipped with multiple antennas, where each BS constructs a local dissimilarity matrix from the channel state information (CSI). These local dissimilarity matrices are then fused to obtain a single global dissimilarity matrix, which is processed through manifold learning to embed users onto a geometric map. The performance is evaluated in a simulated outdoor environment, and Bayesian optimization is employed on the framework hyperparameters to minimize the mean localization error (MLE). Experimental results demonstrate that the proposed framework achieves an MLE of 6.86% in a circular vicinity of radius 100m, requiring less than 15% of labeled CSI samples.

We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring. ErA jointly learns a compact kerne basis and per-pixel weights, while an error-aware term in Augmented Lagrangian unrolling corrects kernel estimation errors via alternating updates and ResUNet denoisers. It achieves state-of-the-art PSNR/SSIM on DPDD, RealDOF, and RTF, and shows strong generalization on CUHK without ground truth.