arXiv subjects

Haoxuan Wu

Publications and source records attributed to Haoxuan Wu.

10 recordsLinked to original sources

arXiv2026

dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process

dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaussian and count-data smoothing, changepoint detection, time-varying parameter regression, and volatility estimation, with posterior predictive draws available for missing-value imputation and forecasting. Nonstationary time series often contain periods of relative stability punctuated by abrupt changes in mean or variance. dsp offers a common interface for model specification, posterior computation, summarization, and visualization across models that were previously developed separately in the literature. Core functionality is implemented in C++ via Rcpp for computational efficiency. We describe the underlying models, package design, and the main functions for fitting, summarizing, and plotting posterior results.

stat.CO↗

arXiv2026

MagCeptor: Encoding Broadcast-Addressable Logic into Magnetic Receptors

Selectively operating multiple components while simplifying actuation and control hardware is a common challenge in multifunctional mechanical and fluidic systems. Here, we introduce MagCeptor, an addressable magnetic-cluster architecture in which a shared external field conveys commands and supplies actuation energy. Fixed stators establish local magnetic-field gradients, while field-induced mover reorientation modulates the resulting forces to select guide-constrained mechanical outputs. Field-dependent position-orientation energy profiles explain target actuation, non-target retention and passive reset after field removal. The evaluated configuration achieves a maximum normalized force output of 385 mN/mm3. By coupling finite, addressable strokes to mechanical transmissions, accumulators and locks, we demonstrate coordinated rotation and multistep manipulation with retained task states. Hierarchical node-and-channel addressing further enables selective model-fluid release from distributed receivers in ex vivo tissue. These results establish a receiver-side architecture that combines shared magnetic inputs with task-specific mechanisms to organize mechanical work, retain operation states and access spatially distributed functions.

eess.SY↗

arXiv2026

Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models

Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models that score clean, decoded pixels. Scoring an intermediate denoising state therefore requires either finishing the remaining denoising steps or decoding a noisy latent that the reward model cannot reliably interpret. We ask whether a video generator can itself act as the evaluator, discriminating human preferences directly from its noisy latents. We propose PRISM (Preference Representation in Intermediate States of Diffusion Models), a latent video reward model that keeps a pre-trained video diffusion transformer frozen, reads features from its early blocks, and maps them to a timestep-conditioned reward through a lightweight query-based aggregation head with only 21.8M trainable parameters. PRISM outperforms pixel-space reward models at high noise levels on two benchmarks while remaining competitive with 8B-parameter vision-language reward models on clean videos. This noise robustness enables early-stage Best-of-N sampling: selecting among candidates after only a few denoising steps reduces sampling cost by up to 10x and surpasses the strongest pixel-space baseline on each generator at 3-4x lower cost.

cs.CV↗

arXiv2026

Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation

Wireless capsule endoscopy (WCE) enables painless visualization of the gastrointestinal tract, but its diagnostic potential is limited by incomplete mucosal coverage and poor transferability of existing navigation methods across patient anatomies. We propose a transferable, anatomical landmarkguided deep reinforcement learning (AL-DRL) framework for autonomous gastric navigation. Leveraging a lightweight edgecontour-depth fusion module, our policy operates on stable, lowdimensional landmark coordinates rather than high-dimensional video streams, effectively bridging the sim-to-real gap. In simulations across eight patient-derived models, the method achieves over 97% coverage within 50 seconds, significantly outperforming vanilla PPO, SAC, and DQN agents. A two-stage sim-to-real pipeline with an adaptive dynamic programming controller actively mitigates physical disturbances. Ex-vivo experiments demonstrate a mean coverage of 87% and a 53% reduction in procedure time compared with expert manual control.

cs.RO↗

arXiv2024

SBoRA: Low-Rank Adaptation with Regional Weight Updates

This paper introduces Standard Basis LoRA (SBoRA), a novel parameter-efficient fine-tuning approach for Large Language Models that builds upon the pioneering works of Low-Rank Adaptation (LoRA) and Orthogonal Adaptation. SBoRA reduces the number of trainable parameters by half or doubles the rank with the similar number of trainable parameters as LoRA, while improving learning performance. By utilizing orthogonal standard basis vectors to initialize one of the low-rank matrices (either $\mathbf{A}$ or $\mathbf{B}$), SBoRA facilitates regional weight updates and memory-efficient fine-tuning. This results in two variants, SBoRA-FA and SBoRA-FB, where only one of the matrices is updated, leading to a sparse update matrix $\mathrmΔ \mathbf{W}$ with predominantly zero rows or columns. Consequently, most of the fine-tuned model's weights $(\mathbf{W}_0+\mathrmΔ \mathbf{W})$ remain unchanged from the pre-trained weights, akin to the modular organization of the human brain, which efficiently adapts to new tasks. Our empirical results demonstrate the superiority of SBoRA-FA over LoRA in various fine-tuning tasks, including commonsense reasoning and arithmetic reasoning. Furthermore, we evaluate the effectiveness of QSBoRA on quantized LLaMA models of varying scales, highlighting its potential for efficient adaptation to new tasks. Code is available at https://github.com/cityuhkai/SBoRA

cs.AI↗

arXiv2024

Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring

We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends, with robustness to outliers and heteroskedastic noise. Finally, we compare our algorithm against several alternatives to demonstrate its efficacy in diverse simulation scenarios and two empirical examples on the U.S. economy.

stat.ME↗

arXiv2024

Drift vs Shift: Decoupling Trends and Changepoint Analysis

We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth estimates of underlying trends in the presence of complex noise components. However, their inability to shrink exactly to zero inhibits direct changepoint detection. In contrast, penalized likelihood methods are highly effective in locating changepoints. However, they require data with simple patterns in both signal and noise. The proposed decoupling approach combines the strengths of both, i.e. the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators, to provide changepoint analysis in complex, modern settings. The proposed framework is outlier robust and can identify a variety of changes, including in mean and slope. It is also easily extended for analysis of parameter shifts in time-varying parameter models like dynamic regressions. We illustrate the flexibility and contrast the performance and robustness of our approach with several alternative methods across a wide range of simulations and application examples.

stat.ME↗

arXiv2022

Syndrome decoding meets multiple instances

The NP-hard problem of decoding random linear codes is crucial to both coding theory and cryptography. In particular, this problem underpins the security of many code based post-quantum cryptographic schemes. The state-of-art algorithms for solving this problem are the information syndrome decoding algorithm and its advanced variants. In this work, we consider syndrome decoding in the multiple instances setting. Two strategies are applied for different scenarios. The first strategy is to solve all instances with the aid of the precomputation technique. We adjust the current framework and distinguish the offline phase and online phase to reduce the amortized complexity. Further, we discuss the impact on the concrete security of some post-quantum schemes. The second strategy is to solve one out of many instances. Adapting the analysis for some earlier algorithm, we discuss the effectiveness of using advanced variants and confirm a related folklore conjecture.

cs.IT↗

arXiv2022

Interpretable Latent Variables in Deep State Space Models

We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data. The model estimates the observed series as functions of latent variables that evolve non-linearly through time. Due to the complexity and non-linearity inherent in DSSMs, previous works on DSSMs typically produced latent variables that are very difficult to interpret. Our paper focus on producing interpretable latent parameters with two key modifications. First, we simplify the predictive decoder by restricting the response variables to be a linear transformation of the latent variables plus some noise. Second, we utilize shrinkage priors on the latent variables to reduce redundancy and improve robustness. These changes make the latent variables much easier to understand and allow us to interpret the resulting latent variables as random effects in a linear mixed model. We show through two public benchmark datasets the resulting model improves forecasting performances.

stat.ML↗

arXiv2021

A News-based Machine Learning Model for Adaptive Asset Pricing

The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

q-fin.ST↗