arXiv:2603.28784v1 Announce Type: cross Abstract: This Data Descriptor presents a fully open, multi-modal dataset for estimating vertical ground reaction force (vGRF) from consumer-grade Apple Watch sensors with laboratory force plate ground truth. Ten healthy adults aged 26--41 years performed five activities: walking, jogging, running, heel drops, and step drops, while wearing two Apple Watches positioned at the left wrist and waist. The dataset contains 492 validated trials with time-aligned inertial measurement unit (IMU) recordings (approximately 100 Hz) and force plate vGRF (Force\_Z, 1000 Hz). The release includes raw and processed time series, trial-level metadata, quality-control flags, and machine-readable data dictionaries. Trial-level matching manifests link recordings across modalities using stable identifiers. Of the 492 validated trials, 395 are triad-complete, containing wrist, waist, and force plate data, enabling cross-sensor analyses and reproducible model evaluation. Dataset quality is characterised through a three-phase cross-sensor plausibility and consistency framework, repeatability analysis of peak vGRF (intraclass correlation coefficient 0.871--0.990), and systematic checks of force ranges and trial completeness. Monte Carlo sensitivity analysis showed that correlation-based validation metrics were robust to single-sample timing perturbations at the IMU sampling resolution. All data are released under CC BY 4.0, with analysis scripts archived alongside the dataset and mirrored on GitHub. This resource supports reproducible research in wearable biomechanics, benchmarking of machine learning models for vGRF estimation, and investigation of sensor placement effects using widely available consumer wearables.

arXiv:2510.05761v2 Announce Type: replace Abstract: Memes are a central part of online culture, yet their virality remains difficult to predict, especially in cross-lingual settings. We present a large-scale, time-series dataset of 46,578 Reddit memes collected from 25 meme-centric subreddits across eight language groups, with more than one million engagement tracking points. We propose a data-driven definition of virality based on a Hybrid Score that normalises engagement by community size and integrates dynamic features such as velocity and acceleration. This approach directly addresses the field's reliance on static, simple volume-based thresholds with arbitrary cut-offs. Building on this target, we construct a multimodal feature set that combines Visual, Textual, Contextual, Network, and Temporal signals, including structured annotations from a multimodal LLM to scale cross-lingual content labelling in a consistent way. We benchmark interpretable baselines (XGBoost, MLP) against end-to-end deep models (BERT, InceptionV3, CLIP) across early observation windows from 30 to 420 minutes. Our best model, a multimodal XGBoost classifier, achieves a PR AUC of 0.43 at 30 minutes and 0.80 at 420 minutes, indicating that early prediction of meme virality is feasible even under strong class imbalance. The results reveal a clear Content Ceiling, where content-only and deep multimodal baselines plateau at low PR AUC, while structural Network and Temporal features are necessary to surpass this limit. A SHAP-based temporal analysis further uncovers an evidentiary transition, where early predictions are dominated by network priors (author and community context), and later predictions increasingly rely on temporal dynamics (velocity, acceleration) as engagement accumulates. Overall, we reframe meme virality as a dynamic, path-dependent process governed by exposure and early interaction patterns rather than by intrinsic content alone.