arXiv subjects

Sam Lin

Publications and source records attributed to Sam Lin.

4 recordsLinked to original sources

arXiv2025

Optical Propulsion and Levitation of Metajets

The quintessential hallmark distinguishing metasurfaces from traditional optical components is the engineering of subwavelength meta-atoms to manipulate light at will. Enabling this freedom, in a reverse manner, to control objects constituted by metasurfaces could expand our capability of optical manipulation to go beyond the predominant microscopic and sub-microscopic scales. Here, we introduce a driving metaphotonic force fully controllable by meta-atoms to manipulate structured objects named metajets. Upon Newton's law of motion that can apply to classical and relativistic mechanics, we develop a first-principles theory to analyze optical forces generated by refraction and reflection at an interface. We find that three-dimensional motions of metajets would be possible if one could introduce an extra wavevector component. We achieve that by creating a spatially distributed phase gradient with deliberately arranged silicon nanopillars. Our experiments and simulations reveal an in-plane propulsion and, very importantly, out-of-plane levitation of the metajets, aligning well with the theory. We also find that the metaphotonic force augments with increased light power but is not limited by the size of metajets, which could unleash new opportunities for metaphotonic control in large settings, such as interstellar light sails.

physics.optics↗

arXiv2025

ADO: Automatic Data Optimization for Inputs in LLM Prompts

This study explores a novel approach to enhance the performance of Large Language Models (LLMs) through the optimization of input data within prompts. While previous research has primarily focused on refining instruction components and augmenting input data with in-context examples, our work investigates the potential benefits of optimizing the input data itself. We introduce a two-pronged strategy for input data optimization: content engineering and structural reformulation. Content engineering involves imputing missing values, removing irrelevant attributes, and enriching profiles by generating additional information inferred from existing attributes. Subsequent to content engineering, structural reformulation is applied to optimize the presentation of the modified content to LLMs, given their sensitivity to input format. Our findings suggest that these optimizations can significantly improve the performance of LLMs in various tasks, offering a promising avenue for future research in prompt engineering. The source code is available at https://anonymous.4open.science/r/ADO-6BC5/

cs.LG↗

arXiv2025

Gigahertz directional light modulation with electro-optic metasurfaces

Active metasurfaces promise spatiotemporal control over optical wavefronts, but achieving high-speed modulation with pixel-level control has remained an unmet challenge. While local phase control can be achieved with nanoscale optical confinement, such as in plasmonic nanoparticles, the resulting electrode spacings lead to large capacitance, limiting speed. Here, we demonstrate the operation of a gigahertz-tunable metasurface for beam steering through local control of metasurface elements in a plasmonic-organic hybrid architecture. Our device comprises a corrugated metallic slot array engineered to support plasmonic quasi-bound states in the continuum (quasi-BICs). These plasmonic quasi-BICs provide ideal optical confinement and electrical characteristics for integrating organic electro-optic (OEO) materials like JRD1 and have not been previously utilized in optical metasurfaces. We obtain a quasi-static resonance tunability of 0.4 nm/V, which we leverage to steer light between three diffraction orders and achieve an electro-optic bandwidth of ~4 GHz, with the potential for further speed improvements through scaling rules. This work showcases on-chip spatiotemporal control of light at the sub-micrometer and gigahertz level, opening new possibilities for applications in 3D sensing and high-speed spatial light modulation.

physics.optics↗

arXiv2024

EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

Cloud-based Large Language Models (LLMs) such as ChatGPT have become increasingly integral to daily operations. Nevertheless, they also introduce privacy concerns: firstly, numerous studies underscore the risks to user privacy posed by jailbreaking cloud-based LLMs; secondly, the LLM service providers have access to all user data, which deters individuals from confidently utilizing such services. To address such concerns, we propose a simple yet effective paradigm, EmojiPrompt, to protect user privacy. At its core, EmojiPrompt performs generative transformation, obfuscating private data within prompts with linguistic and non-linguistic elements before submitting them to cloud-based LLMs. We evaluate EmojiPrompt's performance across 8 datasets from various domains. We also propose simulated inference attacks to assess EmojiPrompt's ability to preserve user privacy. The results demonstrate that EmojiPrompt effectively obfuscates user private data, while largely maintaining, or even enhancing, performances compared to the unobfuscated version. Furthermore, EmojiPrompt's atomic-level obfuscation allows it to function exclusively with cloud-based LLMs. For source code, please refer to: https://github.com/agiresearch/EmojiCrypt.

cs.CL↗