arXiv subjects

Tim G. Zhou

Publications and source records attributed to Tim G. Zhou.

3 recordsLinked to original sources

arXiv2026

Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors

Machine learning models for remote sensing are trained and deployed on a static set of modalities. However, as we equip newer satellites with novel sensors and retire old ones, practitioners may wish to deploy an existing model on a substitution, superset, or subset of modalities with minimal retraining given data availability or practical computational constraints. We study the setting of updating existing models to changing modalities and identify three main scenarios: Modality Transfer (substitution), Addition (superset), and Peeking (subset). We propose DeluluNet, an architecture with modular components for all three changing modality scenarios. DeluluNet is trained end-to-end, learning a multi-modal model from a unimodal teacher and unlabeled multimodal data via modality hallucination--predicting missing modality representations from those that are present. As a result, DeluluNet can keep predicting even when input modalities change, providing a practical alternative to re-labeling and re-training in a changing world.

cs.CV↗

arXiv2026

Haptically Experienced Animacy Facilitates Emotion Regulation: A Theory-Driven Investigation

Emotion regulation (ER) is essential to mental well-being but often difficult to access, especially in high-intensity moments or for individuals with clinical vulnerabilities. While existing technology-based ER tools offer value, they typically rely on self-reflection (e.g., emotion tracking, journaling) or co-regulation through verbal modalities (reminders, text-based conversational tools), which may not be accessible or effective when most needed. The biological role of the touch modality makes it an intriguing alternate pathway, but empirical evidence is limited and under-theorized. Building on our prior theoretical framework describing how a comforting haptic co-regulating adjunct (CHORA) can support ER, we developed a zoomorphic robot CHORA with looped biomimetic breathing and heartbeat behaviors. We evaluated its effects in a mixed-methods in-lab study (N=30), providing physiological, self-report, custom questionnaire, and retrospective interview data. Our findings demonstrate the regulatory effects of haptically experienced animacy, corroborate prior work, and validate CHORA's {theoretically grounded} potential to facilitate four ER strategies.

cs.HC↗

arXiv2025

Asymmetric Duos: Sidekicks Improve Uncertainty

The go-to strategy to apply deep networks in settings where uncertainty informs decisions--ensembling multiple training runs with random initializations--is ill-suited for the extremely large-scale models and practical fine-tuning workflows of today. We introduce a new cost-effective strategy for improving the uncertainty quantification and downstream decisions of a large model (e.g. a fine-tuned ViT-B): coupling it with a less accurate but much smaller "sidekick" (e.g. a fine-tuned ResNet-34) with a fraction of the computational cost. We propose aggregating the predictions of this Asymmetric Duo by simple learned weighted averaging. Surprisingly, despite their inherent asymmetry, the sidekick model almost never harms the performance of the larger model. In fact, across five image classification benchmarks and a variety of model architectures and training schemes (including soups), Asymmetric Duos significantly improve accuracy, uncertainty quantification, and selective classification metrics with only ${\sim}10-20\%$ more computation.

cs.LG↗