Aiden Li

Amogh Vinaykumar, Aiden Li, Suozhi Huang et al.

Visual posters are a compact medium for communicating dense information, yet progress on automatic poster generation remains difficult to measure because existing evaluations are often restricted to paper-only inputs, narrow domains, or surface-level visual similarity. We introduce Any2Poster Bench, a benchmark for any-source poster generation that evaluates systems across eight input modalities--PDFs, URLs, PPTX, DOCX, Markdown, LaTeX, notebooks, and videos--and five content domains. Any2Poster Bench pairs each source with quiz-based probes of verbatim factual retention and interpretive understanding, together with VLM-based judgments of visual quality, layout, readability, content completeness, and logical flow, enabling reproducible assessment of both information fidelity and visual communication. To instantiate and validate this benchmark, we further present Any2Poster Agent, an end-to-end reference agent that parses heterogeneous sources, organizes salient content, plans poster layouts, renders posters, and iteratively refines them using visual feedback. On Any2Poster Bench, Any2Poster Agent achieves 87.25% average accuracy across input modalities and 87.28% across content domains. On PaperQuiz-style evaluation, where prior paper-to-poster agents are directly comparable, Any2Poster Agent improves over PosterAgent-4o from 51.06-51.33% to 72.58% overall accuracy and from 116-121 to 145.16 in density-augmented score. Together, Any2Poster Bench and Any2Poster Agent provide a reusable evaluation resource and a competitive baseline for studying multimodal, domain-general poster generation.

Xiru Wang, Aiden Li, Hongzhao Tan et al.

Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine learning framework that uses electroencephalography to predict treatment efficacy in patients with chronic neck pain, with the goal of supporting individualized therapy and reducing the burden on healthcare systems. The framework centers on a rigorous data preprocessing stage tailored to the characteristics of each EEG recording type. For resting-state EEG, the preprocessing pipeline comprises baseline signal removal, bad channel identification and exclusion, re-referencing, bandpass and notch filtering, Independent Component Analysis, and power spectral density analysis. For motor execution and motor imagery recordings, the same initial steps are applied, after which signals are aligned to trigger events so that event-related desynchronization (ERD) and event-related synchronization (ERS) can be quantified. Synchronously recorded electromyography data are bandpass filtered and smoothed with a moving average, then correlated with the corresponding EEG channels to characterize the EEG EMG relationship during attempted movement. In parallel, we performed an extensive literature review of machine learning models applied to clinical EEG (763 records initially screened, 16 patient and 47 healthy-control studies retained), to inform the post-processing strategy. Through this combined preprocessing and review effort, we aim to develop a robust predictive model that can support personalized healthcare strategies in chronic pain management.