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20242026
most citedHarnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part I - Field-based Semianalytical Synthesis

1 citations · 1 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2026

Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation

Ido Nitzan Hidekel, Dan Raviv

Catastrophic forgetting in continual adaptation is usually studied through parameter drift, replay, or distillation, but these views do not identify which output-space directions a…

physics.app-ph2026

Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part II - Generative Inverse Design (MetaMamba)

Natanel Nissan, Sherman W. Marcus, Dan Raviv +2

We present a generative framework for inverse design of five-layer transmissive Huygens' metasurfaces (HMSs), addressing a longstanding challenge in achieving full-phase, high-effi…

physics.app-ph2026★ 1 cited

Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part I - Field-based Semianalytical Synthesis

Sherman W. Marcus, Natanel Nissan, Vinay K. Killamsetty +4

Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metal…

cs.SD2025

SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

Ido Nitzan Hidekel, Gal lifshitz, Khen Cohen +1

Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs. A central reason is spectral bias, the tendency of neural networks to learn low-frequency…

cs.LG2025

L-SR1: Learned Symmetric-Rank-One Preconditioning

Gal Lifshitz, Shahar Zuler, Ori Fouks +1

End-to-end deep learning has achieved impressive results but often relies on large labeled datasets, exhibits limited generalization to unseen scenarios, and incurs substantial com…

eess.IV2025

Systole-Conditioned Generative Cardiac Motion

Shahar Zuler, Gal Lifshitz, Hadar Averbuch-Elor +1

Accurate motion estimation in cardiac computed tomography (CT) imaging is critical for assessing cardiac function and surgical planning. Data-driven methods have become the standar…

physics.optics2025

Inverse Design of Diffractive Metasurfaces Using Diffusion Models

Liav Hen, Erez Yosef, Dan Raviv +2

Metasurfaces are ultra-thin optical elements composed of engineered sub-wavelength structures that enable precise control of light. Their inverse design - determining a geometry th…

eess.IV2024

CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insights

Shahar Zuler, Shai Tejman-Yarden, Dan Raviv

The ability to map left ventricle (LV) myocardial motion using computed tomography angiography (CTA) is essential to diagnosing cardiovascular conditions and guiding interventional…

cs.CV2024

Synthetic Data Generation for 3D Myocardium Deformation Analysis

Shahar Zuler, Dan Raviv

Accurate analysis of 3D myocardium deformation using high-resolution computerized tomography (CT) datasets with ground truth (GT) annotations is crucial for advancing cardiovascula…