Over-the-air computation (AirComp) has emerged as a promising approach for massive data aggregation, which is yet challenged by the channel variations, task distributions, and inherent energy limitation of the computation nodes. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted Aircomp system to serve multi-cluster computation tasks over time, where the UAV mobility-facilitated spatial and time diversity is exploited for efficient and accurate data computation. Specifically, we aim for the minimization of AirComp aggregation error and the energy consumption by jointly optimizing the transceiver beamforming, normalizing factors, sensor scheduling, and UAV trajectory. To solve the formulated problem, we decompose it into two layers where the inner layer addresses the optimization-based AirComp transceiver design, and the outer layer focuses on the deep reinforcement learning (DRL)-based scheduling and trajectory design. In particular, a pointer network actor-critic learning is developed to tackle the binary scheduling problem, and a soft actor-critic DRL algorithm is employed to determine the UAV trajectory. Simulation results validate the convergence of the proposed hierarchical learning framework and demonstrate its significant performance gains in terms of AirComp aggregation error and energy consumption as compared with baseline schemes.

Improvements in the dynamic range and sensitivity of digital MEMS microphones are essential in applications like advanced noise canceling and voice recognition. A cost effective solution to achieve these goals is the companding ADC architecture. Companding ADCs split the dynamic range in several segments with different quantization noise levels, relaxing power constraints. A common problem of companding microphones are audible artifacts generated when the input signal crosses the boundaries between different amplitude segments. We show in this paper a companding ADC architecture that mitigates the boundary artifacts by leveraging the instantaneous and high-resolution time-domain representation of the input signal in a VCO-based ADC. The use of a multi-rate frequency-to-digital converter allows to decouple quantization noise from the VCO frequency, keeping standard audio sampling rates. Co-optimization of the driver and oscillator circuits enables our VCO-ADC to reach \textgreater 112dBc of peak SFDR without a feedback DAC, keeping a Giga-Ohm input impedance compatible with a capacitive MEMS. We show measurements of a 0.13 $μ$m ASIC implementing a complete readout circuit for a digital MEMS microphone. This includes two analog channels and the digital signal processing and calibration blocks required to deliver a standard single-bit PDM output. This ADC reaches a dynamic range of 114.3dB with a power budget under 400 uW, a Schreier FoM_{SNDR} of 171.0 dB and a FoM_{DR} of 191.3 dB.

Objective video quality metrics commonly assume uniform spatial attention, an assumption that conflicts with the selective nature of human visual perception, particularly in sports videos. Here, allocating more bits for salient regions through semantic encoding can lead to significant bitrate savings. We present a Perceptually-Weighted Video Quality Metric (PW-VQM), a full-reference metric that accounts for the unequal perceptual importance of spatial regions and therefore targets quality evaluation for asymmetrically encoded content. SSIM maps computed in a multiscale wavelet domain are weighted by differentiating between foreground and background regions. Perceptually salient foreground regions are identified by combining open-vocabulary object detection with optical flow analysis, and are assigned higher weight during quality aggregation. Evaluated on sports video content, PW-VQM achieves a Spearman Rank Order Correlation Coefficient of 0.9511, outperforming established metrics including SSIM, VMAF, FUNQUE, and LPIPS. An ablation study confirms the individual contributions of the components of the perceptual weighting.

Flow matching (FM) enables high-fidelity generation, while self-supervised learning (SSL) speech models provide hierarchical representations spanning acoustic and phonetic levels. However, existing FM-based speech enhancement (SE) methods operate primarily in the spectral domain, treating SSL features only as external conditions rather than modeling directly in the SSL latent space. To fully exploit the structural richness of SSL representations, we propose PhASE-Flow, an FM-based SE framework that operates entirely in the SSL space. It models the conditional distribution of clean acoustic representations given phonetic ones, reconstructing the waveform via a neural vocoder. Experiments show that PhASE-Flow outperforms state-of-the-art baselines in perceptual quality and intelligibility. Notably, it achieves competitive performance with only four sampling steps, enabling highly efficient inference. Audio demos are available at https://anonymous.4open.science/w/phase-flow_demo-E6E1/.

An FFT-based direction-of-arrival (DOA) and range-estimation framework for a monostatic uniform linear array (ULA) operating at 77 GHz is presented. A narrowband sinusoidal waveform is used to derive the spatial phase model, determine an aliasing-free inter-element spacing, and select the aperture required to obtain a boresight angular resolution of 2 degree. The resulting design uses an element spacing of 0.97 mm and 58 antenna elements, corresponding to an aperture length of 56.42 mm. Numerical results show accurate angular estimation for a single target at 30 degree and for multiple simultaneous targets. The analysis is further extended to two-dimensional localization by replacing the narrowband waveform with a 1 GHz sinc-modulated signal, which provides an approximate range resolution of 0.15 m. Additional simulations quantify the effects of additive complex Gaussian noise, increased antenna spacing, and target decorrelation on the DOA response.

Downlink channel state information (CSI) feedback plays a key role in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems. The growth of antennas in ultra-massive MIMO increases the difficulty and overhead of CSI feedback, which poses significant challenges for conventional downlink CSI feedback mechanisms. To address the limitations of existing CSI feedback approaches, this paper proposes a novel curvelet learning based framework termed SwinCANet, comprising a frequency-domain information processing module and a denoising module. The frequency-domain information processing module employs curvelet transform to decompose CSI into low-frequency and high-frequency components. Subsequently, Swin Transformer and channel-wise attention block are utilized for extracting the low-frequency and high-frequency representations, respectively, thereby enhancing reconstruction quality. Notably, an additional Swin Transformer facilitates the fusion of multi-scale frequency components, enhancing capabilities across different angular resolutions and spatial directions. Furthermore, we develop a variant (De-SwinCANet), which employs a Sigmoid threshold function to effectively suppress noise coefficients, thereby mitigating various channel impairments and nonlinear distortions. Numerical simulation results demonstrate that the proposed methodology achieves superior performance compared to existing benchmarks while maintaining robust performance under challenging propagation conditions.

Integrated Sensing and Communications (ISAC) is a defining feature of 6G, extending cellular networks with radar-like sensing at limited additional overhead. In bistatic deployments, sensing requires coordinating the transmitter (TX) and receiver (RX) arrays to scan the Cartesian product of angle of departure and arrival, resulting in a four-dimensional sampling problem in the angular domain. This work establishes a complete angular sampling framework for bistatic ISAC, extending the DFT-based optimal-sampling methodology to the full azimuth and elevation domains of both arrays. We show that the bistatic geometry couples the TX and RX elevation angles, and represent this coupling through the ortho-baseline coarray, a virtual array that captures the joint elevation aperture of the array pair. From the coarray we derive a minimal sampling and interpolation scheme, near-lossless and realizable with any beamforming architecture. Monte Carlo simulations confirm the proposed minimal acquisition essentially equalizes the detection accuracy of dense oversampled imaging while acquiring 3 to 5 times fewer TX-RX direction pairs. This allows having bistatic operations with drastically reduced overhead on the radio resource usage of ISAC systems.

We propose a novel approach to the synchronization paradigm in distributed ISAC (DISAC) systems in doubly-dispersive (DD) channel environments via a joint synchronization and radar parameter estimation framework. The proposed method exploits the structure of the system model, which can be linearized in order to apply a bivariate Gaussian belief propagation (GaBP) algorithm that jointly estimates the time offset (TO) and carrier frequency offset (CFO) of each base station (BS), as well as the delay and Doppler parameters of the DD channel in conventional orthogonal frequency division multiplexing (OFDM) systems. Simulation results demonstrate the effectiveness of the proposed algorithm, showing that the radar parameter estimates (i.e., range and velocity) and synchronization parameter estimates (i.e., TO and CFO) approach the Cramér Rao lower bound (CRLB) even at low-to-moderate signal-to-noise ratio (SNR) regimes.

Synthetic data has the potential to be a valuable resource for training machine learning models, particularly Automatic Speech Recognition (ASR) Systems; however, its effectiveness requires systematic evaluation. In this study, we investigate the impact of incorporating synthetic speech data alongside real-world recordings for three Indic languages: Hindi, Kannada, and Telugu. We analyze the performance gains achieved by augmenting synthetic data with real data and independently examine how ASR performance varies with the sources of scripts used to generate synthetic speech. In addition, we evaluate the effect of synthetic speech generated using different speech synthesis models. Finally, we study the impact of voice cloning in synthetic speech generation on ASR performance, including how performance varies with the number of distinct cloned voices used during data generation.

In this paper, we investigate an active-RIS (ARIS)-aided integrated sensing and communication (ISAC) system with Rydberg Atomic REceiver (RARE). Leveraging the magnitude-only and real-domain observation structure of RARE, we first derive a unified ISAC model, along with a closed-form Cramer-Rao bound (CRB) for direction-of-arrival (DoA) estimation. Based on this formulation, we propose a joint design of the {base station (BS)} beamforming and ARIS reflection coefficients to minimize the CRB under RARE-specific signal-to-interference-noise-ratio (SINR) and ARIS power constraints. To tackle the resulting highly non-convex problem, we develop an alternating optimization (AO) framework that combines semidefinite relaxation (SDR) for beamforming and a majorization-minimization (MM)-based approach for ARIS design. Numerical results demonstrate that the proposed RARE-aware framework significantly outperforms conventional RF-based designs and achieves performance close to the radar-only benchmark, highlighting the potential of RARE for quantum-enhanced ISAC with ARIS.

Introduction: To develop and evaluate Fine-UNETR, a Vision Transformer-based architecture for automated segmentation of PSMA-avid lesions on whole-body PET/CT, and to assess clinical utility of AI-derived tumor burden biomarkers for overall survival stratification in radioligand therapy. Methods: In this retrospective study, 373 PSMA PET/CT scans (mean age, 71+-8 years) from patients with prostate cancer were analyzed. Fine-UNETR, a modified UNETR with 8x8x8 voxel patch embedding and axial sliding window training, was trained on 299 scans and validated on 74 scans. Overall survival stratification was assessed in an independent cohort of 67 pre-radioligand therapy patients using Kaplan-Meier analysis and log-rank testing. External validation was performed on 192 cases from the AutoPET IV PSMA PET/CT dataset. Results: Fine-UNETR achieved a Dice similarity coefficient (DSC) of 66.63%, sensitivity of 70.27%, precision of 67.77%, and a lesion detection rate of 79.53% (96.05% for lesions with SUVmax >= 5). On the external validation dataset, the model achieved a DSC of 44.11% and a lesion detection rate of 87.18%, indicating that lesion detection performance was preserved despite reduced voxel-level overlap. AI-derived biomarkers showed excellent agreement with ground truth (total tumor volume: r=0.984; total lesion uptake: r=0.989; lesion count: r=0.960). In the clinical cohort, total tumor volume (p=0.0019), SUVmax (p=0.014), and SUVmean (p=0.016) significantly stratified overall survival. Conclusion: Fine-UNETR enables accurate automated whole-body PSMA lesion segmentation and tumor burden quantification. Performance on an external dataset demonstrates robustness despite evidence of domain shift. AI-derived biomarkers significantly stratified overall survival in a pre-radioligand therapy cohort, supporting the clinical utility of automated PSMA PET/CT quantification for prognostication.

A compact dynamic omnidirectional array is proposed for antenna-level physical-layer security through directional modulation. Unlike conventional directional-modulation transmitters based on phased-array beam synthesis or multiple RF chains, the proposed architecture uses a single RF input and a switching-controlled four-element printed meander-line monopole array operating at 5.05 GHz. The state-dependent excitation introduces controllable magnitude and phase perturbations in the radiated field, producing angle-dependent constellation distortion and bit error rate behavior. Reliable information recovery is confined to a narrow broadside region in the E-plane, whereas the H-plane remains quasi-static and omnidirectional, providing a full 360-degree information-recoverable region. The antenna is implemented on a single-layer Rogers RO4350B substrate with a compact footprint of 0.57 x 1.11 lambda_0^2. A four-path switching network based on commercial RF components is used for experimental validation. Communication measurements using 16-QAM at 5.05 GHz demonstrate BER-defined E-plane information beamwidths of 30 to 36 degrees for calibrated switching modes under a BER <= 10^-3 criterion, while no bit errors are observed in the measured H-plane and the SNR remains above approximately 33 dB. Feed-phase offsets are also used to steer the BER-defined information-recoverable sector, demonstrating information-beam steering with the same antenna-level switching mechanism. These results show that compact antenna-level directional modulation can provide angularly selective information recovery in one principal plane while preserving omnidirectional coverage in the orthogonal plane.

Affine frequency division multiplexing (AFDM) is an emerging chirp-based multicarrier waveform with strong diversity in doubly selective channels, but practical systems suffer from transmitter and receiver IQ imbalance, causing image interference and performance degradation. This paper proposes a two-stage IQ imbalance estimation and compensation method for AFDM systems. First, a preamble-assisted iterative algorithm estimates the time-invariant IQ imbalance parameters by exploiting their slowly time-varying nature. Then, a joint channel estimation and data detection scheme combines basis expansion model (BEM)-based channel estimation with an improved LMMSE detector for interference suppression. Simulations show rapid convergence and near-ideal BER performance.

Electrochemical Impedance Spectroscopy (EIS) is a widely used, non-destructive technique for characterizing electrochemical systems, and its analysis typically relies on fitting the measured spectra to an Equivalent Circuit Model (ECM). Selecting an appropriate ECM, however, remains a major bottleneck: knowledge-based selection requires expert judgment and is difficult to reproduce, while existing automated approaches either choose from a fixed set of candidate circuits or, in the case of Gene Expression Programming, require repeated equivalent-circuit fitting and a predetermined circuit scale. Here, we propose a machine learning method that estimates an ECM directly from an impedance spectrum by representing the circuit as a serialized string of symbols and generating this string with a Long Short-Term Memory (LSTM) network coupled to a convolutional feature extractor. Because the LSTM inherently handles variable-length sequences, the method produces the circuit topology directly, without any fitting during estimation nor prior assumption for the number of elements. A fourth-root transformation of the impedance is introduced to emphasize the mid-frequency features essential for distinguishing circuits, and an adaptive beam search yields multiple ranked candidates. Evaluated on 100,000 synthetic datasets generated from 119 circuit topologies with 1% added noise on impedances, the method identified the correct topology as the most probable ECM in 77.8% of cases and among the top five candidates in 98.8% of cases, with an average estimation time of 17.8 milliseconds per dataset - several orders of magnitude faster than reported fitting-based approaches. These results indicate that direct topology generation with a neural network is a promising route toward fully automated, expert-independent ECM estimation.

In this study, we focus on cough-based tuberculosis screening (CBTS) and hypothesize that fusing speech/audio foundation representations with spectral descriptors will yield stronger screening performance. We expect this fusion to reveal complementary strengths: spectral features preserve fine-grained short-time acoustic detail in cough signals, while foundation embeddings capture higher-level temporal and event-level patterns learned from large-scale pretraining. To this end, we propose COBALT, a novel fusion framework based on codebook-aligned hyperbolic prototypes and bandit-style reliability weighting to integrate heterogeneous representations effectively. Using the CODA TB DREAM Challenge benchmark, COBALT consistently outperforms individual representations and a concatenation baseline, achieving the best overall performance when fusing MFCC with PaSST thereby establishing a new state-of-the-art on the benchmark.

Abscisic acid (ABA) is a central plant hormone for coordinating responses to drought, salinity, cold stress, pathogen attack, wounding, and developmental aging. This paper reviews the biological stimuli that increase ABA biosynthesis, the main production sites and pathways, and the long-distance movement of ABA through plant vascular tissues. It then discusses experimental quantification approaches, including gas-liquid chromatography with electron-capture detection and high-performance liquid chromatography with ultraviolet detection. Finally, the paper presents a molecular-communication-inspired model of ABA transport in which root-side ABA release is represented as a transmitter, the xylem pathway as a bounded channel, and soybean tissue as a receiver. MATLAB Brownian-motion simulations are used to evaluate the effects of released molecule quantity and receiver radius on the detected ABA signal. The results show that higher release quantities produce smoother and stronger reception trends, while larger receivers increase molecule-capture probability.

This paper studies indoor tracking from wall-mounted backscatter fiducials in corridor segments outside direct transmitter illumination. In the measured setup, the transmitter-to-fiducial links are NLOS, whereas the fiducial-to-receiver links along the corridor are largely LOS. The main challenge is that the effective fiducial response is deployment-dependent, so a fixed calibrated link budget is not reliable. We therefore use a grid-based penalized-likelihood tracker that profiles the receiver path, a fitted log-distance slope parameter, and fiducial-specific offsets directly from received powers. The resulting paths can then be reused as surrogate calibration coordinates for residual-map correction, while the same correction with measured calibration coordinates is reported only as a reference. On a short four-fiducial corridor segment, the profiled dual-band tracker gives a 0.52 m median error without measured calibration coordinates, and surrogate residual correction improves this to 0.46 m. With measured calibration coordinates, the same correction and a RADAR-style fingerprint reference both reach 0.31 m. The main remaining limitation is therefore the quality of the surrogate calibration paths rather than the structured observation model itself.

Passive backscatter devices (BDs) can enable indoor non-line-of-sight (NLOS) positioning by serving as virtual anchors whose Doppler-separated signatures are observable in standard channel estimates. This paper studies continuous user-equipment (UE) tracking in corridor environments using a noncoherent power-domain formulation that avoids BD phase synchronization and remains robust to residual carrier offsets and strong multipath. The BD-dependent measurements are modeled by a log-distance law with unknown BD-specific offsets, which allows passive asynchronous devices to be used as anchors without transmit-power calibration. Based on this model, we develop a corridor-constrained maximum a posteriori (MAP) tracker with motion regularization and Huber-robust estimation. In ray-tracing-inspired simulations, the method achieves median positioning errors of 0.23--0.27 m with 90th-percentile errors below 0.45 m. In office-corridor measurements with four passive BDs at 866 MHz, it attains an aggregated median error of 0.505 m and outperforms a simple weighted-average baseline. The results show that passive asynchronous BDs can provide practical sub-meter indoor NLOS tracking while remaining compatible with existing channel-estimation pipelines and energy-autonomous BD deployments.

This paper presents a pilot-aided study of multiple-input multiple-output (MIMO) channel identification and linear deconvolution under spatially correlated Gaussian noise. A real-valued $4\times4$ baseband model is analyzed for both memoryless and finite-impulse-response channels. The noise process is generated from a Toeplitz covariance matrix, the channel is estimated from pilot symbols through maximum-likelihood/least-squares formulations, and the empirical mean-square error is compared with the Cramer--Rao bound. The estimated channel is then used for data-symbol recovery through maximum-likelihood zero-forcing and linear minimum-mean-square-error deconvolution. The results show that sufficiently long and well-conditioned pilot blocks allow the channel estimator to approach the theoretical lower bound, whereas short training intervals cause rank and conditioning limitations, especially for the four-tap model. The deconvolution experiments further show that MMSE regularization provides a more stable inverse than unregularized zero forcing at low signal-to-noise ratios and for inaccurate channel estimates.

Integrated sensing and communication is an important technology for sixth-generation (6G) mobile networks, enabling the joint use of communication and radar sensing within a unified system. While offering significant benefits in terms of spectral efficiency, ISAC introduces new security challenges. In particular, the joint use of resources for sensing and communication can increase vulnerability to eavesdropping and information leakage. In this paper, we study an uplink Non-Orthogonal Multiple Access (NOMA) system where the base station (BS) simultaneously receives user data and senses a potential eavesdropper (Eve) with uncertain location. To enhance the physical-layer security, a robust sensing signal is designed to both sense and jam Eve. We formulate a joint optimization problem that aims to maximize the users' sum rate and the BS sensing performance while maintaining security against Eve. Since the resulting optimization problem is non-convex, we develop an iterative alternating optimization (AO) algorithm that decomposes it into two tractable subproblems. In the first subproblem, the receive combining vectors are optimized in closed form using generalized eigenvalue decomposition. In the second subproblem, the transmit beamforming matrices and sensing power are jointly optimized via semidefinite relaxation (SDR) and successive convex approximation (SCA). Simulation results demonstrate the effectiveness of our solution in terms of fast convergence and resource allocation.

In distant microphones, broadband (BB) methods for direction-of-arrival (DoA) estimation are more suitable than narrowband (NB) methods. Due to the aggregation of their optimization function across all frequency bands, BB estimators are robust to spatial aliasing, a known problem in processing distant microphone data. In NB methods, DoA estimation is performed by utilizing \textit{local} information in each frequency band and hence the estimation is affected by spatial aliasing. However, unlike BB methods, NB methods exploit frequency sparsity to estimate the DoAs of \textit{multiple speakers} in a \textit{single time frame}. In this article, a method to improve the robustness of a NB DoA estimator to spatial aliasing is developed. The proposed method is based on cross-correlation of speech-present time-frequency regions obtained by single frequency filtering (SFF) of the microphone signals. The SFF spectrum is chosen because SFF components have regions of high signal-to-noise ratio both in time and frequency and because speech and non-speech discrimination is robust to degradations in the SFF domain. The proposed NB estimator is compared to four state-of-the-art estimators (one NB and three BB) using detection and accuracy metrics on simulated and real-world data in different reverberation and noise conditions. The results show that in all the environments, the SFF-based NB approach outperforms the state-of-the-art NB approach. Furthermore, the performance of the SFF-based approach is better than some of the BB estimators.

Robust direction-of-arrival (DoA) estimation from noisy and reverberant microphone signals remains challenging. Conventional estimators such as generalized cross-correlation (GCC) and its variants operate in the short-time Fourier transform (STFT) domain, where spectral features primarily reflect vocal-tract characteristics. Recent single frequency filtering (SFF)-based estimators instead use a time-frequency representation that provides high spectral resolution of harmonics along with high temporal resolution of excitation-source events, such as epoch-like impulses. Since excitation-source features have been shown to be more robust to noise and reverberation than spectral features, this work proposes an improved SFF-based DoA estimator that correlates the envelopes of SFF outputs across microphone channels using PHAT-weighted GCC. We further provide a comprehensive evaluation of SFF-based and state-of-the-art GCC-based estimators using publicly available real-room recordings under challenging reverberant, multi-speaker, and noise-corrupted conditions. Experimental results show that the proposed method and an existing SFF-based estimator achieve detection and accuracy performance that is superior or comparable to the best GCC-based estimator across all test cases. We also demonstrate that using speech-dominant bins improves GCC-PHAT robustness, motivating future incorporation of such weighting strategies into SFF-based DoA estimation.

In this work, we study zero-shot cross-lingual speech-based Alzheimer's disease detection (SADD). We hypothesize that learning language-invariant multimodal representations by fusing multilingual speech and text pretrained models is essential for reliable transfer to unseen languages, as the two modalities capture complementary acoustic and linguistic markers of cognitive impairment while adversarial learning suppresses language-specific confounds. Empirical results in zero-shot cross-lingual evaluation substantiate the hypothesis, showing that multimodal fusion consistently outperforms unimodal baselines. To this end, we propose ORBIT, a novel framework that combines cross-attentive fusion, multi-tap language adversaries, and complementary spherical--hyperbolic geometric learning with consensus clustering. Across settings, ORBIT achieves the strongest performance compared to unimodal models and simple concatenation-based fusion baselines.

Numerical integration plays a central role in inertial navigation systems, where sensor measurements are propagated through time to obtain orientation, velocity, and position states. The accuracy of this propagation depends on the numerical integrator type, order and step-size. Prior work showed that for second-order systems with known forcing functions, the gauge freedom in the variation of parameters technique can be exploited to reduce truncation error without modifying the integrator. However, this approach requires analytical knowledge of the forcing function, limiting its applicability in real-world systems. To address this limitation we propose the u-space methodology, a novel state mapping that generalizes the gauge freedom to systems with unknown forcing functions. The optimal gauge is derived in closed form for second-order systems and in both closed and empirical form for first-order systems. The proposed approach was evaluated through Monte Carlo simulations across four forcing functions, five sensor grades, and four Adams-Bashforth orders, as well as on a real-world inertial navigation dataset. Results show consistent error reduction across all tested conditions, with the largest gains observed in the full inertial mechanization pipeline, making the approach applicable to high-grade inertial systems, where truncation error constitutes a larger share of the error budget, and to aided low-cost systems with high-rate updates, where propagation spans only short inter-update intervals.

The presence of interfering scatterers fundamentally changes the design principle for MIMO sensing. Unlike the target-only case, where MIMO sensing sequence design reduces to optimizing the transmit sample covariance, this paper shows that scatterer-induced signal-dependent interference makes the Bayesian Fisher information depend on the full temporal precoding sequence. Consequently, for the MIMO sensing problem with scatterers using the Bayesian Cramér-Rao lower bound (BCRLB) as the objective, the entire sensing sequence must be designed explicitly, instead of just the precoding matrix. This paper considers such a precoding sequence design problem under hardware constraint for MIMO sensing for estimating the azimuth angles of multiple targets based on the prior information of both the targets and the scatterers. We formulate a worst-case BCRLB minimization across multiple target angles, yielding a max-min fractional program under constant-modulus or constant-norm hardware constraints. We further develop a constant-norm linear transform that converts the ratio objectives into linear forms, leading to an iterative algorithm with closed-form precoder updates. The framework extends to joint precoder-combiner design and multi-stage sensing with adaptive prior refinement. Numerical results demonstrate the effectiveness and the efficiency of the proposed algorithm, revealing sweeping-like beampatterns that illuminate target angular regions while suppressing interference from the scatterers.

Whether connected units are similar because influence spreads across ties or because similar units form ties, is a long-standing problem. Contagion or influence is generically unidentified from observational network data. We consider the minimal and common setting of a single network, fixed over time, with two waves of a binary nodal outcome. Rather than positing a parametric model for network formation, we reframe identification of contagion as a selection-bias problem and develop a sensitivity framework. We define a controlled direct effect (CDE) holding a tie present while intervening on an alter's outcome. We show that the gap between the CDE and the observed connected-dyad risk ratio is governed by how strongly a latent homophily variable shifts the composition of connected dyads. Inspired by Smith-style selection-bias sensitivity analysis and the risk-ratio bounding function of Ding and VanderWeele we develop interpretable nonparametric bounds. This translates the question "is there contagion?" into the question "how strong would latent homophily have to be to explain away the observed contagion?" A simulation study characterizes the bounds' error control and power. We apply the framework to the 2008 U.S. House votes on the Troubled Asset Relief Program, identifying under which assumptions contagion is plausible.

We introduce an amortized Bayesian framework for spatial boundary detection that generalizes posterior inference across areal graphs with varying numbers of regions and diverse adjacency structures. The underlying model couples a Poisson count likelihood with a covariate-driven rule to interrupt smoothing across dissimilar neighboring areas, utilizing a directed acyclic graph autoregressive (DAGAR) prior to capture residual spatial dependence. To approximate the target posterior distribution, a neural engine is trained on simulated maps: a permutation-invariant summary network encodes graph-aware representations of the observed counts, offsets, covariates, and adjacency matrices, while a conditional normalizing flow generates the approximate posterior draws. Simulation studies demonstrate accurate parameter recovery, near-nominal interval coverage, well-calibrated posterior predictive behavior, and informative posterior boundary probabilities. Benchmarking against Markov chain Monte Carlo (MCMC) confirms close agreement regarding primary boundary evidence, and an ablation study validates the inclusion of model-guided graph summaries. Finally, applications to Glasgow respiratory disease and California lung cancer data demonstrate that a single trained neural engine can be seamlessly deployed across real-world maps with distinct graph structures, yielding boundary conclusions consistent with established localized smoothing analyses.

Alzheimer's disease is characterized by the progressive accumulation of amyloid-$β$ and tau followed years later by cognitive impairment. Despite this established motif, substantial subject-level variability exists in the age of pathological progression and the onset of cognitive symptoms. To understand the source of this variation, subjects must be aligned across heterogeneous disease timelines via frameworks that jointly model disease progression and time to cognitive impairment with reference to landmark positivity thresholds. Existing neurodegenerative disease progression models rely on restrictive parametric forms, fail to anchor disease timelines to positivity thresholds, and decouple biomarker trajectories from cognitive survival endpoints. To address these limitations, we introduce the Bayesian Threshold-Aligned Joint Disease Progression Model (B-TAJ DPM). This generative, semi-parametric framework models multivariate disease progression trajectories over latent disease timelines anchored at landmark positivity thresholds. Crucially, the framework integrates a survival model to link pathological progression to cognitive impairment. Posterior inference and posterior predictions for unseen subjects are carried out in open-source software. Simulation studies demonstrate excellent estimation accuracy and interval coverage. When applied to Alzheimer's Disease Neuroimaging Initiative data, B-TAJ DPM characterizes non-linear progression patterns, quantifies subject-level variation in positivity age, and reveals links between age of tau positivity and acceleration of cognitive impairment.

Geostatistical spatial prediction for environmental processes is typically undertaken using Gaussian process models via Kriging, while machine learning (ML) algorithms are state-of-the-art for non-spatial prediction. An exciting recent fusion of these ideas imbibes traditional ML algorithms with the capacity to deal with spatial autocorrelation, leading to improved predictive performance. A range of approaches have been proposed, including fusion with Gaussian processes, observation-driven correlation structures, spatial basis functions and local geographical fitting. However, there has been no numerical comparison of their relative predictive performances, which is needed to advise environmental scientists on the optimal approach to use. This paper fills this knowledge gap, and focuses on random forests as the ML algorithm because they are more computationally and conceptually straightforward to implement than deep learning algorithms. The results from two studies are presented, the first being a controlled simulation experiment investigating whether any single approach is consistently superior across different spatial autocorrelation types. The second study focuses on the prediction of air pollution concentrations within a tuberculosis prevalence study in Blantyre, Malawi. The results show that whilst no single approach is universally superior, utilising spatial basis functions appears to perform consistently well across both the simulation and real data studies.

Understanding urban mobility patterns is crucial for designing efficient and sustainable transportation systems. Motivated by an application to the municipality of Padova and its surroundings, we propose a novel statistical framework for the analysis and clustering of mobility trajectories derived from telephonic data. We introduce a compositional representation of individual movements that integrates the uncertain device location with information on the surrounding road network, encoding at each time point the proportions of different road types compatible with the observed position. This formulation naturally accounts for measurement uncertainty and yields trajectories evolving in the simplex. To model these data, we develop a state-space framework for compositional time series that captures both the telephonic measurement error and the temporal dynamics of the latent mobility process. Building on this representation, we propose a model-based clustering approach based on mixtures of state-space models to identify groups of trajectories with similar evolution. This allows us to aggregate individual movements into interpretable mobility patterns at the population level. The results of the case study demonstrate the ability of the approach to uncover meaningful mobility behaviors, providing insights that are potentially relevant to policy makers.