Eunbin Lee | Searcharxiv

arXiv2026

High-Fidelity Transcranial Ultrasound Multi-focal Stimulation via Physics-Aware Hologram Technique

Transcranial ultrasound stimulation (TUS) offers non-invasive deep-brain neuromodulation with high spatial precision, but reliably generating complex multi-target acoustic fields through the skull remains challenging. Here, we introduce a physics-aware hologram technique that directly generates fabrication-ready holographic implementations while preserving consistency between numerical field synthesis and physical acoustic realization. The method enables single-, dual-, and tri-focal transcranial stimulation patterns and was validated through in silico simulations, ex vivo skull measurements, and in vivo experiments. Compared with representative state-of-the-art methods, the proposed approach improved focal reconstruction and energy confinement at intended targets while reducing off-target acoustic leakage. In a neuropathic-pain mouse model, simultaneous bilateral stimulation of thalamic nuclei reduced c-Fos expression and showed preliminary improvements in pain-related behavioral responses. These findings support the use of fabrication-consistent holographic design for spatially localized and reproducible multi-target transcranial neuromodulation.

eess.SY↗

arXiv2026

VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization

Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While video provides rich temporal variation, dense in-the-wild streams with strong ego-motion create a co-occurrence trap: foreground objects and background context move coherently, encouraging representations to collapse into scene encoders. To address this, we propose VINO (Video-driven Invariance for Non-Contextual Objects), a teacher-student framework that learns robust image encoders from dense video by imposing a structural information bottleneck. Using a class-agnostic structural prior solely to generate views-not as semantic pseudo-labels-VINO forms an asymmetric distillation problem. The teacher predicts from a foreground-union view with the background suppressed, while the student observes object-conditioned scene views that retain surrounding context but remove competing instances. Matching these targets via masked distillation makes background cues unreliable, pushing the representation toward object-centric invariances. We further enforce temporal object permanence via teacher-anchored cross-time distillation over track-matched objects, and stabilize part-to-whole consistency with mask-guided local views. Through attention visualization and unsupervised object discovery on PASCAL VOC, we demonstrate that VINO effectively disentangles foreground from background. Pretrained on the dense Walking Tours Venice video, VINO achieves 34.8 CorLoc, yielding highly focused, shape-biased representations that substantially outperform prior dense-video and motion-guided SSL baselines.

cs.CV↗