arXiv subjects

Nan Cui

Publications and source records attributed to Nan Cui.

6 recordsLinked to original sources

arXiv2026

Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters

Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require extra parameters fine-tuning, or suffer from optimization instability. We propose a lightweight and scalable bias mitigation method that combines a kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. Our approach estimates a closed-form projection that removes single or multiple sensitive attributes from LLM representations with no additional trainable parameters. To preserve task utility, we introduce a two-level MoE adapter that selectively restores useful signals without reintroducing bias. Experiments on two public datasets show that our method reduces attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy.

cs.LG↗

arXiv2025

Scalable, universal and conformal direct electrodes microprinting for high-performance van der Waals-integrated two-dimensional electronics and flexible applications

Two-dimensional (2D) materials with extraordinary electrical properties, hold promising for large-scale, flexible electronics. However, their device performance could be hindered due to the excessive defects introduced via traditional electrode integration processes. Transfer printing techniques have been developed for van der Waals contacts integration, while existing techniques encounter limitations in achieving conformal electrode transfer and compatibility with flexible devices. Here we introduce a highly conformal microprinting technique utilizing polypropylene carbonate (PPC)/Polyvinyl alcohol (PVA) copolymer, which enables successful transfer of wafer-scale, micropatterned electrodes onto diverse substrates, including those with complex geometries. This technique, implemented with 2D transition metal dichalcogenides (TMDCs), yields 2D field-effect transistors with near-ideal ohmic contacts, and a record-high carrier mobility up to 334 cm2 V-1 s-1 for a WSe2 device. Furthermore, we fabricated transistor arrays on MoS2 thin film, which show uniform device performance. We also present the flexible MoS2 transistors that not only achieve a high electron mobility of up to 111 cm2 V-1 s-1 but also exhibit outstanding mechanical robustness. Our findings represent a significant leap forward in the fabrication of flexible 2D electronics, paving the way for numerous emerging technologies.

physics.app-ph↗

arXiv2025

Ultrathin Ga$_2$O$_3$ Tunneling Contact for 2D Transition-metal Dichalcogenides Transistor

The development of two-dimensional (2D) transition metal dichalcogenides (TMDs) based transistors has been constrained by high contact resistance and inadequate current delivery, primarily stemming from metal-induced gap states and Fermi level pinning. Research into addressing these challenges is essential for the advancing 2D transistors from laboratory experiments to industrial-grade production. In this work, we present amorphous Ga$_2$O$_3$ as a novel tunneling contact layer for multilayer WS2-based field-effect transistors (FETs) to enhance electrical performance. The addition of this innovative tunneling layer avoid Schottky barrier forming while finally change into a tunneling barrier with the barrier height to just 3.7 meV, near-ideal ohmic contacts. This approach effectively reduces contact resistance to only 2.38 k$\Omega\,\mu$m and specific contact resistivity as low as $3 \times 10^{-5}$ $\Omega$cm$^2$. A record-high electron mobility of 296 cm$^2$ V$^{-1}$ s$^{-1}$ and ON-OFF ratio over 106 are realized for WS$_2$ transistor at room temperature. Compared to other tunneling materials, ultrathin Ga$_2$O$_3$ layer offers scalability, cost-efficient production and broad substrate compatibility, making it well-suited for seamless integration with industrial wafer-scale electronics. A robust device performance remains highly consistent in a large-scale transistor array fabricated on $1.5\times 1.5$ cm$^2$ chips, with the average mobility closing to 200 cm$^2$ V$^{-1}$ s$^{-1}$. These findings establish a new benchmark for contact performance in 2D transistors and prove the potential of tunneling contact engineering in advancing high-performance, scalable 29 pelectronics with promising applications in quantum computing and communication.

cond-mat.mes-hall↗

arXiv2024

Zero-Shot Code Representation Learning via Prompt Tuning

Learning code representations has been the core prerequisite of many software engineering tasks such as code clone detection and code generation. State-of-the-art program representation techniques mainly utilize pre-trained language models (PLMs) such as CodeBERT. A Transformer encoder is firstly pre-trained on a large-scale code corpus to acquire general knowledge about source code. The pre-trained model is then fine-tuned on specific tasks using an amount of labeled data. However, gathering training samples for the downstream tasks can be prohibitively expensive and impractical for domain-specific languages or project-specific tasks. Besides, pre-training and downstream tasks are usually heterogeneous, which makes it difficult to fully explore the knowledge learned during pre-training. In this paper, we propose Zecoler, a zero-shot approach for learning code representations. Zecoler is built upon a pre-trained programming language model. In order to elicit knowledge from the PLMs efficiently, Zecoler casts the downstream tasks to the same form of pre-training objectives by inserting train-able prompts into the original input. These prompts can guide PLMs on how to generate better results. Subsequently, we employ the prompt tuning technique to search for the optimal prompts for PLMs automatically. This enables the representation model to efficiently fit the downstream tasks through fine-tuning on the dataset in source language domain and then reuse the pre-trained knowledge for the target domain in a zero-shot style. We evaluate Zecoler in five code intelligence tasks including code clone detection, code search, method name prediction, code summarization, and code generation. The results show that our approach significantly outperforms baseline models under the zero-shot setting.

cs.SE↗

arXiv2023

Equipping Federated Graph Neural Networks with Structure-aware Group Fairness

Graph Neural Networks (GNNs) have been widely used for various types of graph data processing and analytical tasks in different domains. Training GNNs over centralized graph data can be infeasible due to privacy concerns and regulatory restrictions. Thus, federated learning (FL) becomes a trending solution to address this challenge in a distributed learning paradigm. However, as GNNs may inherit historical bias from training data and lead to discriminatory predictions, the bias of local models can be easily propagated to the global model in distributed settings. This poses a new challenge in mitigating bias in federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair Federated Graph Neural Network, that enhances group fairness of federated GNNs. As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN aims to mitigate both types of bias under federated settings. First, we provide theoretical insights on the connection between data bias in a training graph and statistical fairness metrics of the trained GNN models. Based on the theoretical analysis, we design $\text{F}^2$GNN which contains two key components: a fairness-aware local model update scheme that enhances group fairness of the local models on the client side, and a fairness-weighted global model update scheme that takes both data bias and fairness metrics of local models into consideration in the aggregation process. We evaluate $\text{F}^2$GNN empirically versus a number of baseline methods, and demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both fairness and model accuracy.

cs.LG↗

arXiv2022

Zero-Shot Program Representation Learning

Learning program representations has been the core prerequisite of code intelligent tasks such as code search and code clone detection. The state-of-the-art pre-trained models such as CodeBERT require the availability of large-scale code corpora. However, gathering training samples can be costly and infeasible for domain-specific languages such as Solidity for smart contracts. In this paper, we propose Zecoler, a zero-shot learning approach for code representations. Zecoler is built upon a pre-trained programming language model. In order to elicit knowledge from the pre-trained models efficiently, Zecoler casts the downstream tasks to the same form of pre-training tasks by inserting trainable prompts into the original input. Then, it employs the prompt learning technique which optimizes the pre-trained model by merely adjusting the original input. This enables the representation model to efficiently fit the scarce task-oriented data while reusing pre-trained knowledge. We evaluate Zecoler in three code intelligent tasks in two program languages that have no training samples, namely, Solidity and Go, with model trained in corpora of common languages such as Java. Experimental results show that our approach significantly outperforms baseline models in both zero-shot and few-shot settings.

cs.SE↗