arXiv subjects

Hsin-Hsiung Huang

Publications and source records attributed to Hsin-Hsiung Huang.

At least 19 recordsLinked to original sources

arXiv2026

Voxel-wise Bayesian Estimation for Multi-Population Positronium Lifetime Imaging

Positronium lifetime imaging (PLI) provides local annihilation environment data beyond conventional activity imaging. Existing approaches, however, often estimate lifetime parameters over predefined regions, neglecting multiple lifetime populations within the same location. We present a 3D, population-specific Bayesian framework for fast voxel-wise PLI. A partial system matrix describes the spatial probability of detected events, while measured lifetimes provide soft assignments to slow, fast, and noise populations. These event responsibilities are used to estimate a decay-rate posterior independently for each voxel, preserving local lifetime variation and statistical uncertainty. In simulations, our formulation recovered spatially varying slow-population decay rates and a common fast-population rate, whereas a single-population model produced systematic bias. Slow-population two-standard-deviation coverage ranged from 93.8% to 98.1%. Experimental validation using 124I triple-coincidence data from a Siemens Biograph Vision Quadra scanner produced separate slow- and fast-population maps for aluminum, nickel, copper, and quartz. The long-lived quartz component matched ortho-positronium, while the fast population showed material-dependent differences among metals. Fast-population coverage was lower (69.1%), indicating underestimated uncertainty. The method is highly efficient, requiring only seconds to minutes per population on a single CPU core. This framework provides fast, population-specific PLI with Bayesian uncertainty quantification, making spatially resolved statistical inference feasible and practical for volumetric applications.

stat.AP↗

arXiv2026

Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings

Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, developing reliable conversion predictive models is difficult to develop due to limited longitudinal data availability We evaluate TabPFN (Tabular Pre-Trained Foundation Network) against traditional machine learning methods for predicting 3 year MCI to AD conversion using the TADPOLE dataset derived from ADNI. Using multimodal biomarker features extracted from demographics, APOE4, MRI volumes, CSF markers, and PET imaging, we conducted an experimental comparison across varying training set sizes (N=50 to 1000) and models including XGBoost, Random Forest, LightGBM, and Logistic Regression. TabPFN achieved one the highest performance (AUC=0.892), outperforming LightGBM (AUC=0.860) and demonstrating advantages in low data settings. At N=50 training samples, TabPFN maintained strong AUC while the traditional machine learning models struggles at small training samples. These findings demonstrate that foundation models are promising for disease prediction in data limited scenarios, such as Alzheimers diseases.

cs.AI↗

arXiv2026

A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis

Replicated weighted networks often exhibit many structural zeros alongside heterogeneous non-zero edge strengths. In structural connectomics, this zero-inflation coincides with subjects expressing overlapping, rather than discrete, connectivity patterns. To address these features, we propose a Bayesian adaptive latent mixture model for zero-inflated weighted networks. Our approach represents each subject network as a simplex mixture of shared low-rank latent score matrices, integrated with a hurdle likelihood that separates edge existence from conditional edge strength. A sparsity-coupling parameter enables absent edges to be either independent of, or informative about, the latent connectivity. For computation, we employ transformed Hamiltonian Monte Carlo on unconstrained coordinates, selecting the number of templates via predictive fit, held-out link prediction, and template stability. Theoretically, we establish posterior consistency, local asymptotic normality, a Bernstein--von Mises approximation, and predictive consistency for an identifiable quotient-space estimand under a fixed-template scenario. Simulations demonstrate performance gains over topology-only baselines in settings with mixed memberships or structure-informed sparsity. Applied to Human Connectome Project data, the model recovers stable latent score patterns and heterogeneous subject-level mixtures, with behavioural analyses serving strictly as exploratory annotations rather than confirmatory biomarker claims.

stat.ME↗

arXiv2026

Bayesian low-rank latent-cluster regression for mixed health outcomes

High-dimensional health and surveillance studies often involve many collinear predictors, multiple correlated outcomes of different types, and latent heterogeneity across observational units. We propose a Bayesian latent-cluster reduced-rank regression model for multivariate mixed outcomes. The model is a finite mixture of regression surfaces: each latent cluster has a cluster-specific mean shift and a low-rank coefficient matrix, yielding simultaneous clustering, dimension reduction, and component-wise interpretability. Response coordinates may be Gaussian, Bernoulli, or negative binomial. Multiplicative gamma process shrinkage adapts the effective rank within each cluster, and a WAIC-based criterion is used to tune the number of clusters and the nominal maximal rank. We establish posterior contraction for the identifiable component-specific regression surfaces and mean shifts, up to label permutation, and derive corresponding contraction for predictor-side singular subspaces. We also analyze the default label-invariant reporting pipeline based on the posterior similarity matrix: an eigenspace embedding followed by mean shift is shown to consistently recover the latent partition under an additional strong separation margin. Simulation experiments spanning all-Gaussian, all-Bernoulli, all-negative-binomial, and mixed Gaussian--Bernoulli--negative-binomial regimes show accurate recovery of the number of clusters and competitive clustering performance against $K$-means, mclust, PCA-based clustering, and a Gaussian reduced-rank mixture benchmark. We illustrate the method in three applications that show how the model separates individual-level utilization groups and produces interpretable county- and state-level cluster maps together with response-specific posterior predictive maps.

stat.ME↗

arXiv2026

HIMCE: High-dimensional multiple imputation via covariance-mode updating for neuroimaging and spatiotemporal blocks

High-dimensional neuroimaging and spatiotemporal blocks often contain structured missingness from acquisition artifacts, preprocessing failures, and sensor dropout. Multiple imputation propagates uncertainty, but fully conditional specification methods such as multivariate imputation by chained equations (MICE) can be slow or unstable when block dimension is large and correlations are strong. A multivariate normal (MVN) working model provides a coherent posterior predictive target and an exact data augmentation sampler, but repeated covariance sampling and matrix factorizations become costly in large dimensions. We propose High-dimensional Imputation via covariance Mode and Chained Equations (HIMCE), a hybrid multiple-imputation procedure for continuous blocks. Relative to exact MVN data augmentation, HIMCE preserves the Gaussian conditional imputation law and propagates mean- parameter uncertainty through stochastic coefficient or local-ridge draws. In high-dimensional blocks, it approximates covariance uncertainty through covariance-mode updating, optionally with a scalar bridge; in small blocks, it can restore exact covariance uncertainty through a conditional inverse-Wishart refresh. We record the exact Bayesian reference sampler and prove fixed-dimensional posterior consistency and asymptotic equivalence of mode plug-in prediction in total variation. We also develop diagnostics based on randomized rank-cell probability integral transform (PIT), PIT-consistent empirical coverage, and marginal distribution overlays. In the primary spatial benchmark, HIMCE improves posterior-mean error relative to HIMA and screened MICE, runs at HIMA-like speed and below half the MICE runtime, and improves interval coverage over HIMA, although MICE remains better calibrated. A repeated low- dimensional NHANES illustration shows improved coverage with competitive point prediction.

stat.ME↗

arXiv2026

Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework

Cognitive decline is highly heterogeneous across individuals, which complicates prognosis, trial design, and treatment planning. We present the Personalized Cognitive Decline Assessment Digital Twin (PCD-DT), a multimodal and uncertainty-aware framework for modeling patient-specific disease trajectories from sparse, noisy, and irregular longitudinal data. The framework combines three methodological components: (1) latent state-space models for individualized temporal dynamics, (2) multimodal fusion for clinical, biomarker, and imaging features, and (3) uncertainty-aware validation and adaptive updating for robust digital twin operation. We also outline how conditional generative models can support data augmentation and stress testing for underrepresented progression patterns. As a preliminary feasibility study, we analyze longitudinal TADPOLE trajectories and show clear separation between cognitively normal and Alzheimer's disease cohorts in ADAS13, ventricle volume, and hippocampal volume over five years. We further conduct a multimodal next-visit prediction ablation using an LSTM sequence model on 3{,}003 visit-pair sequences derived from TADPOLE, where the combined cognitive plus MRI configuration achieves the lowest standardized RMSE for both ADAS13 (0.4419) and ventricle volume (0.5842), outperforming a Last Observation Carried Forward baseline. A Bayesian tensor modeling component for high-dimensional imaging fusion is also discussed. These results support the feasibility of the proposed architecture while also highlighting the need for stronger uncertainty calibration and longer-horizon predictive evaluation. The PCD-DT framework provides a principled starting point for personalized in silico modeling in neurodegenerative disease. This work positions PCD-DT as a foundational step toward clinically deployable, uncertainty-aware digital twin systems.

cs.AI↗

arXiv2026

CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease

Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates multi-modal longitudinal data (cognitive scores, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetics). We use a Transformer-based architecture to fuse these modalities and a Deep Markov Model to capture temporal dynamics. We trained and evaluated the framework using data from 1,666 patients in the TADPOLE (Alzheimer's Disease Neuroimaging Initiative) dataset. We assessed the model for prediction error, demographic fairness, and robustness to missing-not-at-random (MNAR) data patterns. ognitiveTwin provides accurate and personalized predictions of cognitive decline. Its demonstrated fairness across patient demographics and resilience to clinical dropout make it a reliable tool for clinical trial enrichment and personalized care planning.

cs.AI↗

arXiv2026

Bayesian sparse principal coordinates analysis with delta-tolerant linear approximation for microbiome data

Principal coordinates analysis (PCoA) is a standard exploratory tool for microbiome beta-diversity studies, but its axes are defined by pairwise dissimilarities and therefore do not directly identify the taxa driving an ordination. We propose Bayesian sparse principal coordinates analysis (BSPCoA), a post hoc framework that approximates the leading principal coordinates by a sparse linear surrogate in the observed taxa. A delta-tolerance diagnostic quantifies the discrepancy between the classical ordination and its best linear surrogate, clarifying when taxon-level interpretation is well supported. We place three-parameter beta normal global-local priors on the surrogate coefficients to induce row sparsity, obtain posterior uncertainty, and select influential taxa. The method reduces to sparse principal component analysis under Euclidean distance, while remaining applicable to ecologically meaningful dissimilarities such as Bray--Curtis and Hellinger distances. We conduct simulation studies to demonstrate that BSPCoA provides an approximately linear representation of the dominant ordination geometry while enhancing interpretability in sparse microbiome settings. In the Hadza gut microbiome data, the method produces an ordination close to that of classical PCoA while highlighting a parsimonious set of taxa associated with seasonal variation.

stat.ME↗

arXiv2026

A Conjugate Bayesian Framework for Fast 3D Positronium Lifetime Estimation with a Partial System Matrix

Background: Positronium lifetime imaging extends conventional positron emission tomography by using the time interval between positron emission and annihilation as an additional contrast mechanism. Voxel-wise lifetime estimation in fully three-dimensional settings is computationally difficult because the number of feasible detector-time channels grows rapidly, whereas only a small subset is observed in practice. We developed a scalable statistical framework for three-dimensional positronium lifetime estimation based on a time-of-flight-aware partial system matrix restricted to observed detector-time channels, combined with posterior event-to-voxel weighting and a conjugate Gamma--Exponential update for closed-form voxel-wise effective-rate estimation. Results: Restricting the forward model to observed detector-time channels reduced memory and computational requirements while preserving the Poisson data model for retained detected triple coincidences. In simulated data with 4056 voxels, the analytic Bayesian estimator required 2.76 s versus 74.46 s for 10 L-BFGS-B iterations on the same CPU while accurately recovering the effective-rate map. In a triple-coincidence dataset acquired with a J-PET prototype scanner and a NEMA image-quality phantom, a 234 375-voxel effective-rate map was estimated in approximately 3 s from about $3.64\times10^5$ retained events. Conclusions: Restricting the system matrix to observed detector-time channels makes fully three-dimensional positronium lifetime estimation computationally practical for sparse triple-coincidence data. The proposed posterior-weighted conjugate update provides a fast and stable single-component surrogate estimator of voxel-wise effective lifetime for large-scale three-dimensional positronium lifetime imaging.

physics.med-ph↗

arXiv2026

Bayesian Deep Count Regression and Anomaly Detection: Evidence from GDELT Event Panels

The Global Database of Events, Language and Tone (GDELT) provides geolocated event records that can be aggregated into weekly spatiotemporal panels of event counts across regions, actors, and event types. These panels are typically sparse, bursty, and overdispersed, so calibrated probabilistic forecasting is essential for monitoring rare surges. We propose Bayesian count regression pipelines that pair deterministic deep temporal encoders with negative binomial (NB2) and zero-inflated negative binomial (ZINB2) likelihood heads. Posterior predictive simulation yields predictive quantiles and right-tail probabilities that support both forecasting and anomaly scoring. For interpretable spillover attribution, we also fit a Bayesian generalised linear model with high-dimensional lagged cross-series predictors and a two-step screen-and-refit procedure under a three-parameter beta-normal (TPBN) shrinkage prior. To connect spillovers to directional statistics, active cross-region effects are mapped to geodesic bearings on the World Geodetic System 1984 ellipsoid (WGS84) and summarised using weighted circular moments, rose diagrams, and bearing-field maps. Simulations with known spillovers and conflict-panel case studies show accurate right-tail behaviour and a practical workflow for detecting and interpreting geopolitical shocks.

stat.AP↗

arXiv2026

Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype

Digital twins for radiation-based imaging and therapy are most useful when they assimilate patient data, quantify predictive uncertainty, and support clinically constrained decisions. This paper presents a modular framework for actionable digital twins in radiation-based imaging and therapy and instantiates its reproducible open-data component using the \openkbpfull{} benchmark. The framework couples PatientData, Model, Solver, Calibration, and Decision modules and formalizes latent-state updating, uncertainty propagation, and chance-constrained action selection. As an initial implementation, we build a GPU-ready PyTorch/MONAI reimplementation of the \openkbp{} starter pipeline: an 11-channel, 19.2M-parameter 3D U-Net trained with a masked loss over the feasible region and equipped with Monte Carlo dropout for voxel-wise epistemic uncertainty. To emulate the update loop on a static benchmark, we introduce decoder-only proxy recalibration and illustrate uncertainty-aware virtual-therapy evaluation using DVH-based and biological utilities. A complete three-fraction loop including recalibration, Monte Carlo inference, and spatial optimization executes in 10.3~s. On the 100-patient test set, the model achieved mean dose and DVH scores of 2.65 and 1.82~Gy, respectively, with 0.58~s mean inference time per patient. The \openkbp{} case study thus serves as a reproducible test bed for dose prediction, uncertainty propagation, and proxy closed-loop adaptation, while future institutional studies will address longitudinal calibration with delivered-dose logs and repeat imaging.

eess.IV↗

arXiv2025

Maritime Vessel Tracking

The Automatic Identification System (AIS) provides time stamped vessel positions and kinematic reports that enable maritime authorities to monitor traffic. We consider the problem of relabeling AIS trajectories when vessel identifiers are missing, focusing on a challenging nationwide setting in which tracks are heavily downsampled and span diverse operating environments across continental U.S. waters. We propose a hybrid pipeline that first applies a physics-based screening step to project active track endpoints forward in time and select a small set of plausible ancestors for each new observation. A supervised neural classifier then chooses among these candidates, or initiates a new track, using engineered space time and kinematic consistency features. On held out data, this approach improves posit accuracy relative to unsupervised baselines, demonstrating that combining simple motion models with learned disambiguation can scale vessel relabeling to heterogeneous, high volume AIS streams.

stat.AP↗

arXiv2025

Uncertainty quantification for mixed membership in multilayer networks with degree heterogeneity using Gaussian variational inference

Analyzing multilayer networks is central to understanding complex relational measurements collected across multiple conditions or over time. A pivotal task in this setting is to quantify uncertainty in community structure while appropriately pooling information across layers and accommodating layer-specific heterogeneity. Building on the multilayer degree-corrected mixed-membership (ML-DCMM) model, which captures both stable community membership profiles and layer-specific vertex activity levels, we propose a Bayesian inference framework based on a spectral-assisted likelihood. We then develop a computationally efficient Gaussian variational inference algorithm implemented via stochastic gradient descent. Our theoretical analysis establishes a variational Bernstein--von Mises theorem, which provides a frequentist guarantee for using the variational posterior to construct confidence sets for mixed memberships. We demonstrate the utility of the method on a U.S. airport longitudinal network, where the procedure yields robust estimates, natural uncertainty quantification, and competitive performance relative to state-of-the-art methods.

stat.ME↗

arXiv2025

Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics

Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness, and overdispersion of such data cause standard deep learning models, including the Temporal Fusion Transformer (TFT), to produce unreliable long-horizon predictions. We introduce STFT-VNNGP, a hybrid architecture that won the 2023 Algorithms for Threat Detection (ATD) competition by overcoming these limitations. Designed to bridge this gap, our model employs a two-stage process: first, a TFT captures complex temporal dynamics to generate multi-quantile forecasts. These quantiles then serve as informed inputs for a Variational Nearest Neighbor Gaussian Process (VNNGP), which performs principled spatiotemporal smoothing and uncertainty quantification. In a case study forecasting conflict dynamics in the Middle East and the U.S., STFT-VNNGP consistently outperforms a standalone TFT, showing a superior ability to predict the timing and magnitude of bursty event periods, particularly at long-range horizons. This work offers a robust framework for generating more reliable and actionable intelligence from challenging event data, with all code and workflows made publicly available to ensure reproducibility.

stat.ML↗

arXiv2025

A Bayesian Sparse Kronecker Product Decomposition Framework for Tensor Predictors with Mixed-Type Responses

Ultra-high-dimensional tensor predictors are increasingly common in neuroimaging and other biomedical studies, yet existing methods rarely integrate continuous, count, and binary responses in a single coherent model. We present a Bayesian Sparse Kronecker Product Decomposition (BSKPD) that represents each regression (or classification) coefficient tensor as a low-rank Kronecker product whose factors are endowed with element-wise Three-Parameter Beta-Normal shrinkage priors, yielding voxel-level sparsity and interpretability. Embedding Gaussian, Poisson, and Bernoulli outcomes in a unified exponential-family form, and combining the shrinkage priors with Polya-Gamma data augmentation, gives closed-form Gibbs updates that scale to full-resolution 3-D images. We prove posterior consistency and identifiability even when each tensor mode dimension grows subexponentially with the sample size, thereby extending high-dimensional Bayesian theory to mixed-type multivariate responses. Simulations and applications to ADNI and OASIS magnetic-resonance imaging datasets show that BSKPD delivers sharper signal recovery and lower predictive error than current low-rank or sparsity-only competitors while preserving scientific interpretability.

stat.ME↗

arXiv2025

Cyclic-Shift Sparse Kronecker Tensor Classifier for Signal-Region Detection in Neuroimaging

This study proposes a cyclic-shift logistic sparse Kronecker product decomposition (SKPD) model for high-dimensional tensor data, enhancing the SKPD framework with a cyclic-shift mechanism for binary classification. The method enables interpretable and scalable analysis of brain MRI data, detecting disease-relevant regions through a structured low-rank factorization. By incorporating a second spatially shifted view of the data, the cyclic-shift logistic SKPD improves robustness to misalignment across subjects, a common challenge in neuroimaging. We provide asymptotic consistency guarantees under a restricted isometry condition adapted to logistic loss. Simulations confirm the model's ability to recover spatial signals under noise and identify optimal patch sizes for factor decomposition. Application to OASIS-1 and ADNI-1 datasets demonstrates that the model achieves strong classification accuracy and localizes estimated coefficients in clinically relevant brain regions, such as the hippocampus. A data-driven slice selection strategy further improves interpretability in 2D projections. The proposed framework offers a principled, interpretable, and computationally efficient tool for neuroimaging-based disease diagnosis, with potential extensions to multi-class settings and more complex transformations.

stat.ME↗

arXiv2025

Low-Rank Regularization of Global Fréchet Regression Models for Distributional Responses

Fréchet regression has emerged as a useful tool for modeling non-Euclidean response variables associated with Euclidean covariates. In this work, we propose a global Fréchet regression estimation method that incorporates low-rank regularization. Focusing on distribution function responses, we demonstrate that leveraging the low-rank structure of the model parameters enhances both the efficiency and accuracy of model fitting. Through theoretical analysis of the large-sample properties, we show that the proposed method enables more robust modeling and estimation than standard dimension reduction techniques. To support our findings, we also present numerical experiments that evaluate the finite-sample performance.

stat.ME↗

arXiv2025

Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions

Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression, complicating accurate prognosis and personalized care planning. This heterogeneity underscores the critical need for predictive models capable of forecasting patient-specific disease trajectories. Artificial Intelligence (AI) offers powerful tools to address this challenge by analyzing complex, multi-modal, and longitudinal patient data. This paper provides a comprehensive survey of AI methodologies applied to personalized AD progression prediction. We review key approaches including state-space models for capturing temporal dynamics, deep learning techniques like Recurrent Neural Networks for sequence modeling, Graph Neural Networks (GNNs) for leveraging network structures, and the emerging concept of AI-driven digital twins for individualized simulation. Recognizing that data limitations often impede progress, we examine common challenges such as high dimensionality, missing data, and dataset imbalance. We further discuss AI-driven mitigation strategies, with a specific focus on synthetic data generation using Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to augment and balance datasets. The survey synthesizes the strengths and limitations of current approaches, emphasizing the trend towards multimodal integration and the persistent need for model interpretability and generalizability. Finally, we identify critical open challenges, including robust external validation, clinical integration, and ethical considerations, and outline promising future research directions such as hybrid models, causal inference, and federated learning. This review aims to consolidate current knowledge and guide future efforts in developing clinically relevant AI tools for personalized AD prognostication.

cs.LG↗