arXiv subjects

Amr Helmy

Publications and source records attributed to Amr Helmy.

5 recordsLinked to original sources

arXiv2025

Exceptional Points in Hybrid-Plasmonic Quasiparticles for Ultracompact Modulators

Current progress in electro-optical modulation within silicon integrated photonics, driven by the unique capabilities of advanced functional materials, has led to significant improvements in device performance. However, inherent constraints in dimensionality and tunability still pose challenges for further innovation. In this work, we propose a strategy that exploits the principles of non-Hermitian physics--specifically, the concept of exceptional points (EPs)--to transcend these limitations and pave the way for the next generation of versatile, high-performance photonic devices. Our multilayer structure supports hybrid plasmonic waveguide modes that can manifest as various orders of quasiparticles. By judiciously setting spatial parameters, the system can be tuned to exhibit both weak and strong coupling regimes between the plasmonic and dielectric modes, leading to the controlled formation of EP degeneracies. Furthermore, the integration of low-loss phase-change materials (Sb2S3 and Sb2Se3) enables dynamic electrical tuning, resulting in pronounced modulation of propagation loss and transmission coefficients over sub-micron distances. This superior performance not only sets a new benchmark for device responsivity and compactness but also opens promising avenues for future research, including the incorporation of gain media for loss compensation at EPs and the exploration of alternative tunable materials for next-generation ultracompact photonic devices.

physics.optics↗

arXiv2025

Fully Programmable Plasmonic PT-Symmetric Dimer with Epsilon Near Zero and Phase-Change Materials for Integrated Photonics

As photonic systems progress toward enhanced miniaturization, dynamic reconfigurability, and improved energy efficiency, a central challenge endures: the accurate and independent control of optical losses and resonant properties on scalable, CMOS-compatible platforms. To address this challenge, we present a hybrid plasmonic dimer that functions in a non-Hermitian regime, capitalizing on the synergistic interplay between Epsilon Near Zero (ENZ) materials and phase-change materials (PCMs) to achieve superior reconfigurability through electrical modulation. Our approach harnesses non-Hermitian physics by precisely modulating the loss differential among coupled modes alongside their resonant frequencies, thereby steering the system to an Exceptional Point (EP) characterized by emergent phenomena and enhanced perturbation sensitivity. By integrating ENZ materials to control dissipation with PCMs to fine-tune resonant frequencies, our structure achieves robust programmability, delivering at least 16 distinct operational states for coupled resonators. This capability supports deep subwavelength confinement and transitions between EP and non-EP regimes, while the inherently low power consumption of ENZ materials and PCMs under deep-subwavelength confinement offers significant advantages even in high-dimensional configurations. We believe that this work outlines a significant route for next-generation programmable photonics, delivering subwavelength confinement, energy-efficient operation, and high-dimensional optical reconfigurability within an integrated, scalable, and manufacturable platform.

physics.optics↗

arXiv2025

Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning

Geospatial data offers immense potential for understanding our planet. However, the sheer volume and diversity of this data along with its varied resolutions, timescales, and sparsity pose significant challenges for thorough analysis and interpretation. This paper introduces Earth AI, a family of geospatial AI models and agentic reasoning that enables significant advances in our ability to unlock novel and profound insights into our planet. This approach is built upon foundation models across three key domains--Planet-scale Imagery, Population, and Environment--and an intelligent Gemini-powered reasoning engine. We present rigorous benchmarks showcasing the power and novel capabilities of our foundation models and validate that when used together, they provide complementary value for geospatial inference and their synergies unlock superior predictive capabilities. To handle complex, multi-step queries, we developed a Gemini-powered agent that jointly reasons over our multiple foundation models along with large geospatial data sources and tools. On a new benchmark of real-world crisis scenarios, our agent demonstrates the ability to deliver critical and timely insights, effectively bridging the gap between raw geospatial data and actionable understanding.

cs.AI↗

arXiv2025

A Recipe for Improving Remote Sensing VLM Zero Shot Generalization

Foundation models have had a significant impact across various AI applications, enabling use cases that were previously impossible. Contrastive Visual Language Models (VLMs), in particular, have outperformed other techniques in many tasks. However, their prevalence in remote sensing (RS) is still limited, due to the scarcity of diverse remote-sensing visual-language datasets. In this work we introduce two novel image-caption datasets for training of remote sensing foundation models. The first dataset pairs aerial and satellite imagery with captions generated by Gemini using landmarks extracted from Google Maps. The second dataset utilizes public web images and their corresponding alt-text, filtered for the remote sensing domain, resulting in a diverse dataset with greater breadth in image styles and subject matter. These datasets are used to pre-train the MaMMUT~\citep{kuo2023mammutsimplearchitecturejoint} VLM architecture, resulting in state-of-the-art generalization performance in zero-shot cross-modal retrieval on well-known public benchmarks. Finally, we present our ongoing research to distill image-level knowledge gained in the VLM contrastive training procedure to enhance the model's localization ability. Specifically, we iteratively generate pseudo-labels for image regions based on the model's attention maps and use these labels for further training. To mitigate noisy attention maps and create robust segmentation masks, we introduce a novel attention-pooling mechanism called the Smooth-Attention-Operation.

cs.CV↗

arXiv2018

Quantum Sensing for High Energy Physics

Report of the first workshop to identify approaches and techniques in the domain of quantum sensing that can be utilized by future High Energy Physics applications to further the scientific goals of High Energy Physics.

hep-ex↗