arXiv subjects

Fengyi Xu

Publications and source records attributed to Fengyi Xu.

4 recordsLinked to original sources

arXiv2026

Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention

Crowdsourced social media imagery provides real-time visual evidence of urban flooding but often lacks reliable geographic metadata for emergency response. Existing Visual Place Recognition (VPR) models struggle to geo-localize these images due to cross-source domain shifts and visual distortions. We present VPR-AttLLM, a model-agnostic framework integrating the semantic reasoning and geospatial knowledge of Large Language Models (LLMs) into VPR pipelines via attention-guided descriptor enhancement. VPR-AttLLM uses LLMs to isolate location-informative regions and suppress transient noise, improving retrieval without model retraining or new data. We evaluate this framework across San Francisco and Hong Kong using established queries, synthetic flooding scenarios, and real social media flood images. Integrating VPR-AttLLM with state-of-the-art models (CosPlace, EigenPlaces, SALAD) consistently improves recall, yielding 1-3% relative gains and up to 8% on challenging real flood imagery. By embedding urban perception principles into attention mechanisms, VPR-AttLLM bridges human-like spatial reasoning with modern VPR architectures. Its plug-and-play design and cross-source robustness offer a scalable solution for rapid geo-localization of crowdsourced crisis imagery, advancing cognitive urban resilience.

cs.CL↗

arXiv2025

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light

In continuous-variable quantum information processing, it is crucial to develop high-efficiency and broadband quantum memory of squeezed light, which enables the storage of full-bandwidth information. Here, we present a quantum memory of squeezed light with up to 24 MHz bandwidth, which is at least 12 times that of previous narrowband resonant memory systems, via a far-off resonant Raman process. We achieve output squeezing of as high as 1.0 dB with fidelity above 92% and a memory efficiency of 80%, corresponding to an end-to-end efficiency of 64.2%, when input squeezing is 1.6 dB. The lowest excess noise of 0.025 shot-noise-unit in the memory system is estimated by the noisy channel model which is benefited from optimizing quantum memory performance with a backward retrieval strategy. Our results represent a breakthrough in high-performance memory for squeezed states within tens of MHz-level bandwidth, which has potential applications in high-speed quantum information processing.

quant-ph↗

arXiv2021

Monotonic Neural Network: combining Deep Learning with Domain Knowledge for Chiller Plants Energy Optimization

In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems. Compared to the hotspot applications of deep learning (e.g. image classification and NLP), it is difficult to collect enormous data for deep network training in real-world physical systems. Most existing methods reduce the complex systems into linear model to facilitate the training on small samples. To tackle the small sample size problem, this paper considers domain knowledge in the structure and loss design of deep network to build a nonlinear model with lower redundancy function space. Specifically, the energy consumption estimation of most chillers can be physically viewed as an input-output monotonic problem. Thus, we can design a Neural Network with monotonic constraints to mimic the physical behavior of the system. We verify the proposed method in a cooling system of a data center, experimental results show the superiority of our framework in energy optimization compared to the existing ones.

eess.SP↗

arXiv2021

Quantifying quantum coherence of optical cat states

Optical cat state plays an essential role in quantum computation and quantum metrology. Here, we experimentally quantify quantum coherence of an optical cat state by means of relative entropy and l_1 norm of coherence in Fock basis based on the prepared optical cat state at rubidium D1 line. By transmitting the optical cat state through a lossy channel, we also demonstrate the robustness of quantum coherence of optical cat state in the presence of loss, which is different from the decoherence properties of fidelity and Wigner function negativity of the optical cat state. Our results confirm that quantum coherence of optical cat states is robust against loss and pave the way for the application with optical cat states.

quant-ph↗