arXiv subjects

Ashley Wang

Publications and source records attributed to Ashley Wang.

7 recordsLinked to original sources

arXiv2026

Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems

This paper explores the effectiveness of various novel deterministic and traditional stochastic data assimilation (DA) and parameter recovery (PR) algorithms given noisy data from chaotic systems. We use semi-analytic methods to numerically construct synthetic data from the Lorenz '63 and multiscale Lorenz '96 chaotic dynamical systems, adding white noise. Our findings show that, for different noise levels, deterministic PR algorithms paired with deterministic DA algorithms are shown computationally to be overall more accurate and stable than stochastic PR algorithms. Additionally, deterministic PR methods have demonstrated greater speed and efficiency, requiring less computational power than stochastic PR methods. This suggests that future work should consider exploring the full potential of deterministic PR algorithms in the presence of noise.

nlin.CD↗

arXiv2026

SHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs

Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotators. As LLMs are increasingly deployed in real-world settings, faithfully modeling such ambiguity is essential to identify contested inputs, preserve variability in ambiguous cases, and capture the full distribution of human judgments. Yet, existing LLM alignment approaches have predominantly assumed a single correct label, excluding annotator disagreement during optimization. Instead of treating this ambiguity as noise, we show how to treat it as information that improves model behavior through a new algorithm called SMARTLY HANDLING AMBIGUOUS LABELS IN ALIGNING LLMS (SHALA-LLM). This reinforcement learning framework provides a new way for LLMs to learn directly from annotator distributions while dynamically prioritizing highly ambiguous samples during optimization. Experiments on ambiguity-sensitive NLI and ER benchmarks, including ChaosNLI, GoEmotions, and MSP-Podcast, demonstrate that SHALA-LLM improves agreement with annotator label distributions, e.g. on ChaosNLI, it reduces Jensen-Shannon Distance by up to 62.1%. At the same time, SHALA-LLM improves F1 by up to 16.7%, showing that modeling annotator disagreement can also strengthen classification performance.

cs.LG↗

arXiv2026

DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising

Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging. The lack of paired clean-noisy training data, rapid tracer kinetics, and frame-dependent noise variations further limit the effectiveness of existing deep learning denoising methods. We propose DECADE (A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising), an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames. DECADE incorporates temporal consistency during both training and iterative sampling, using noisy frames as guidance to preserve quantitative accuracy. The method was trained and evaluated on datasets acquired from Siemens Vision 450 and Siemens Biograph Vision Quadra scanners. On the Vision 450 dataset, DECADE consistently produced high-quality dynamic and parametric images with reduced noise while preserving myocardial blood flow (MBF) and myocardial flow reserve (MFR). On the Quadra dataset, using 15%-count images as input and full-count images as reference, DECADE outperformed UNet-based and other diffusion models in image quality and K1/MBF quantification. The proposed framework enables effective unsupervised denoising of Rb-82 dynamic cardiac PET without paired training data, supporting clearer visualization while maintaining quantitative integrity.

cs.CV↗

arXiv2025

Ricci Matrix Comparison for Graph Alignment: A DMC Variation

The graph alignment problem explores the concept of node correspondence and its optimality. In this paper, we focus on purely geometric graph alignment methods, namely our newly proposed Ricci Matrix Comparison (RMC) and its original form, Degree Matrix Comparison (DMC). To formulate a Ricci-curvature-based graph alignment situation, we start with discussing different ideas of constructing one of the most typical and important topological objects, the torus, and then move on to introducing the RMC based on DMC with theoretical motivations. Lastly, we will present to the reader experimental results on a torus and a complex protein-protein interaction network that indicate the potential of applying a differential-geometric view to graph alignment. Results show that a direct variation of DMC using Ricci curvature can help with identifying holes in tori and aligning line graphs of a complex network at 80-90+% accuracy. This paper contributes a new perspective to the field of graph alignment and partially shows the validity of the previous DMC method.

cs.SI↗

arXiv2024

Degree Matrix Comparison for Graph Alignment

The graph alignment problem, which considers the optimal node correspondence across networks, has recently gained significant attention due to its wide applications. There are graph alignment methods suited for various network types, but we focus on the unsupervised geometric alignment algorithms. We propose Degree Matrix Comparison (DMC), a very simple degree-based method that has shown to be effective for heterogeneous networks. Through extensive experiments and mathematical proofs, we demonstrate the potential of this method. Remarkably, DMC achieves up to 99% correct node alignment for 90%-overlap networks and 100% accuracy for isomorphic graphs. Additionally, we propose a reduced Greedy DMC with lower time complexity and Weighted DMC that has demonstrated potential for aligning weighted graphs. Positive results from applying Greedy DMC and the Weighted DMC furthermore speaks to the validity and potential of the DMC. The sequence of DMC methods could significantly impact graph alignment, offering reliable solutions for the task.

cs.SI↗

arXiv2024

A Recipe for Success? Exploring Strategies for Improving Non-Visual Access to Cooking Instructions

Cooking is an essential activity that enhances quality of life by enabling individuals to prepare their own meals. However, cooking often requires multitasking between cooking tasks and following instructions, which can be challenging to cooks with vision impairments if recipes or other instructions are inaccessible. To explore the practices and challenges of recipe access while cooking, we conducted semi-structured interviews with 20 people with vision impairments who have cooking experience and four cooking instructors at a vision rehabilitation center. We also asked participants to edit and give feedback on existing recipes. We revealed unique practices and challenges to accessing recipe information at different cooking stages, such as the heavy burden of hand-washing to interact with recipe readers. We also presented the preferred information representation and structure of recipes. We then highlighted design features of technological supports that could facilitate the development of more accessible kitchen technologies for recipe access. Our work contributes nuanced insights and design guidelines to enhance recipe accessibility for people with vision impairments.

cs.HC↗

arXiv2022

Analysis of a JWST NIRSpec Lab Time Series: Characterizing Systematics, Recovering Exoplanet Transit Spectroscopy, and Constraining a Noise Floor

The James Webb Space Telescope's NIRSpec instrument will unveil the nature of exoplanet atmospheres across the wealth of planet types, from temperate terrestrial worlds to ultrahot Jupiters. In particular, the 0.6-5.3 micron PRISM mode is especially well-suited for efficient spectroscopic exoplanet observations spanning a number of important spectral features. We analyze a lab-measured NIRSpec PRISM mode Bright Object Time Series (BOTS) observation from the perspective of a JWST user to understand the instrument performance and detector properties. We create two realistic transiting exoplanet time series observations by performing injection-recovery tests on the lab-measured data to quantify the effects of real instrument jitter, drift, intrapixel sensitivity variations, and 1/$f$ noise on measured transmission spectra. By fitting the time series systematics simultaneously with the injected transit, we can obtain more realistic transit depth uncertainties that take into account noise sources that are currently not modeled by traditional exposure time calculators. We find that sources of systematic noise related to intrapixel sensitivity variations and PSF motions are apparent in the data at the level of a few hundred ppm, but can be effectively detrended using a low-order polynomial with detector position. We recover the injected spectral features of GJ 436 b and TRAPPIST-1 d, and place a 3-sigma upper limit on the detector noise floor of 14 ppm. We find that the noise floor is consistent with <10 ppm at the 1.7-sigma level, which bodes well for future observations of challenging targets with faint atmospheric signatures.

astro-ph.EP↗