arXiv subjects

Qianyi Zhao

Publications and source records attributed to Qianyi Zhao.

6 recordsLinked to original sources

arXiv2026

High-quality Nano-patterning of Oxide Interfaces Using Transferred Gold Mask

Complex oxide interfaces, such as $\mathrm{SrTiO_3}$ and $\mathrm{KTaO_3}$ based heterostructures, host rich correlated phenomena with strong potential for advanced device applications. However, these interfaces are extremely susceptible to contamination and defect formation during nanofabrication, which often compromises device performance. Here, we present a solvent-free method for patterning oxide interfaces by employing high-resolution transferable thin metal masks in conjunction with oxygen-enriched $\mathrm{Ar^+}$ ion milling, which enables a clean and well-controlled nanofabrication process. Transport measurements demonstrate that the fabricated devices preserve their intrinsic properties, including high carrier mobilities, with negligible degradation compared to the pristine interfaces. This technique offers a convenient and robust route for engineering high-performance oxide electronic devices with precisely tailored transport characteristics.

cond-mat.mtrl-sci↗

arXiv2026

Reconfigurable Oxide Nanoelectronics by Tip-induced Electron Delocalization

Reconfigurable oxide nanoelectronics, enabled by conductive atomic force microscope (cAFM) lithography, have established complex oxide interfaces as a promising platform for quantum engineering that harnesses emergent phenomena for advanced functionalities. However, this cAFM nanofabrication process can only occur in the air, with simultaneous device decay described under the "water-cycle" writing mechanism. These restrictions pose ongoing challenges for device optimization in the quantum regime at mK temperatures. Here, we demonstrate a "waterless" cAFM lithography approach that is compatible with vacuum and cryogenic environments. Through oxygen vacancy engineering at the LaAlO$_3$/SrTiO$_3$ interface, we have achieved nonvolatile and reconfigurable cAFM control of nanoscale interfacial polaron-electron liquid transition at mK temperatures with an ultrafine line resolution of 0.85 nm. Supported by first-principles calculations and drift-diffusion modeling, we show that tip-controlled oxygen vacancy electromigration plays a key role. This advancement bridges reconfigurable device fabrication and concurrent characterization in situ at mK temperatures, and establishes a versatile Hubbard toolbox for engineering programmable quantum phases in correlated oxides.

cond-mat.mes-hall↗

arXiv2025

FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models

Efficiently enhancing the reasoning capabilities of large language models (LLMs) in federated learning environments remains challenging, particularly when balancing performance gains with strict computational, communication, and privacy constraints. This challenge is especially acute in healthcare, where decisions-spanning clinical, operational, and patient-facing contexts-demand not only accurate outputs but also interpretable, traceable rationales to ensure safety, accountability, and regulatory compliance. Conventional federated tuning approaches on LLM fail to address this need: they optimize primarily for answer correctness while neglecting rationale quality, leaving CoT capabilities dependent on models' innate pre-training abilities. Moreover, existing methods for improving rationales typically rely on privacy-violating knowledge distillation from centralized models. Additionally, the communication overhead in traditional federated fine-tuning on LLMs remains substantial. We addresses this gap by proposing FedCoT, a novel framework specifically designed to enhance reasoning in federated settings. FedCoT leverages a lightweight chain-of-thought enhancement mechanism: local models generate multiple reasoning paths, and a compact discriminator dynamically selects the most promising one. This approach improves reasoning accuracy and robustness while providing valuable interpretability, which is particularly critical for medical applications. To manage client heterogeneity efficiently, we adopt an improved aggregation approach building upon advanced LoRA module stacking, incorporating client classifier-awareness to achieve noise-free aggregation across diverse clients. Comprehensive experiments on medical reasoning tasks demonstrate that FedCoT significantly boosts client-side reasoning performance under stringent resource budgets while fully preserving data privacy.

cs.CL↗

arXiv2025

A Milli-Kelvin Atomic Force Microscope Made of Glass

Milli-Kelvin atomic force microscopy (mK-AFM) presents an ongoing experimental challenge due to the intense vibrations in a cryogen-free dilution refrigerator and the low cooling power available at mK temperatures. A viable approach is to make the system exceptionally rigid and thermally insulating to decouple external vibrations and isolate heat dissipation from the piezo elements. Here, we present a low-cost and large scan-range mK-AFM that operates below 100 mK. All the essential parts of our mK-AFM, including the scanners, tip assembly, and microscope body, are custom-made of fused silica glass by taking advantage of its high specific modulus, extremely low thermal expansion coefficient, and excellent thermal insulation properties. We carefully balance the scan range (25 $μ$m $\times$ 25 $μ$m), heat dissipation, and stiffness of the system to reach optimal performance at mK temperatures.

physics.ins-det↗

arXiv2024

ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction

The emergence of diffusion models has significantly advanced image synthesis. The recent studies of model interaction and self-corrective reasoning approach in large language models offer new insights for enhancing text-to-image models. Inspired by these studies, we propose a novel method called ArtAug for enhancing text-to-image models in this paper. To the best of our knowledge, ArtAug is the first one that improves image synthesis models via model interactions with understanding models. In the interactions, we leverage human preferences implicitly learned by image understanding models to provide fine-grained suggestions for image synthesis models. The interactions can modify the image content to make it aesthetically pleasing, such as adjusting exposure, changing shooting angles, and adding atmospheric effects. The enhancements brought by the interaction are iteratively fused into the synthesis model itself through an additional enhancement module. This enables the synthesis model to directly produce aesthetically pleasing images without any extra computational cost. In the experiments, we train the ArtAug enhancement module on existing text-to-image models. Various evaluation metrics consistently demonstrate that ArtAug enhances the generative capabilities of text-to-image models without incurring additional computational costs. The source code and models will be released publicly.

cs.CV↗

arXiv2024

FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization

With increasing concerns and regulations on data privacy, fine-tuning pretrained language models (PLMs) in federated learning (FL) has become a common paradigm for NLP tasks. Despite being extensively studied, the existing methods for this problem still face two primary challenges. First, the huge number of parameters in large-scale PLMs leads to excessive communication and computational overhead. Second, the heterogeneity of data and tasks across clients poses a significant obstacle to achieving the desired fine-tuning performance. To address the above problems, we propose FedMCP, a novel parameter-efficient fine-tuning method with model-contrastive personalization for FL. Specifically, FedMCP adds two lightweight adapter modules, i.e., the global adapter and the private adapter, to the frozen PLMs within clients. In a communication round, each client sends only the global adapter to the server for federated aggregation. Furthermore, FedMCP introduces a model-contrastive regularization term between the two adapters. This, on the one hand, encourages the global adapter to assimilate universal knowledge and, on the other hand, the private adapter to capture client-specific knowledge. By leveraging both adapters, FedMCP can effectively provide fine-tuned personalized models tailored to individual clients. Extensive experiments on highly heterogeneous cross-task, cross-silo datasets show that FedMCP achieves substantial performance improvements over state-of-the-art FL fine-tuning approaches for PLMs.

cs.CL↗