arXiv subjects

Shiwen Wu

Publications and source records attributed to Shiwen Wu.

9 recordsLinked to original sources

arXiv2025

Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering

Recent advances in large language models (LLMs) have led to impressive progress in natural language generation, yet their tendency to produce hallucinated or unsubstantiated content remains a critical concern. To improve factual reliability, Retrieval-Augmented Generation (RAG) integrates external knowledge during inference. However, existing RAG systems face two major limitations: (1) unreliable adaptive control due to limited external knowledge supervision, and (2) hallucinations caused by inaccurate or irrelevant references. To address these issues, we propose Know3-RAG, a knowledge-aware RAG framework that leverages structured knowledge from knowledge graphs (KGs) to guide three core stages of the RAG process, including retrieval, generation, and filtering. Specifically, we introduce a knowledge-aware adaptive retrieval module that employs KG embedding to assess the confidence of the generated answer and determine retrieval necessity, a knowledge-enhanced reference generation strategy that enriches queries with KG-derived entities to improve generated reference relevance, and a knowledge-driven reference filtering mechanism that ensures semantic alignment and factual accuracy of references. Experiments on multiple open-domain QA benchmarks demonstrate that Know3-RAG consistently outperforms strong baselines, significantly reducing hallucinations and enhancing answer reliability.

cs.CL↗

arXiv2025

A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation

The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigating hallucinations and improving model accuracy. However, a systematic understanding and comparative analysis of the rapidly emerging KG-RAG methods are still lacking. This paper seeks to lay the foundation for systematically answering the question of when and how to use KG-RAG by analyzing their performance in various application scenarios associated with different technical configurations. After outlining the mind map using KG-RAG framework and summarizing its popular pipeline, we conduct a pilot empirical study of KG-RAG works to reimplement and evaluate 6 KG-RAG methods across 9 datasets in diverse domains and scenarios, analyzing the impact of 9 KG-RAG configurations in combination with 17 LLMs, and combining Metacognition with KG-RAG as a pilot attempt. Our results underscore the critical role of appropriate application conditions and optimal configurations of KG-RAG components.

cs.AI↗

arXiv2023

Tuning Optical Properties of Metamaterials by Mie Scattering for Efficient Sub-ambient Daytime Radiative Cooling

The management of the abundant eggshell biowaste produced worldwide has become a problematic issue due to the generated odor and microorganisms after directly disposing eggshell biowaste in landfills. Herein, we propose a novel method to convert the hazardous eggshell biowaste to valuable resources for energy management applications. Eggshell-based films are fabricated by embedding eggshell powders into polymer matrix to achieve highly efficient sub-ambient daytime radiative cooling. Benefiting from the Mie scattering of eggshell particles/air pores in the solar spectrum and strong emission of eggshell in the mid-infrared (mid-IR) range, the eggshell-based films present high reflection of 0.96 in the solar spectrum and high emission of 0.95 in the mid-IR range, resulting in significant average temperature drop of 5°C and 12°C below the ambient temperature during daytime and nighttime, respectively. Moreover, the eggshell-based films exhibit excellent flexibility and self-cleaning properties, which are beneficial for practical long-term outdoor applications. Our proposed design provides a novel means for an environmentally friendly and sustainable management of the eggshell biowaste.

physics.optics↗

arXiv2022

Tunable Multi-Peak Perfect Absorbers Based on Borophene for High-Performance Near-Infrared Refractive Index Sensing

Borophene has recently attracted significant attention as an emerging two-dimensional monoelemental material for refractive index sensing because of its ultra-high surface-to-volume ratio and outstanding surface sensitivity. However, current research mainly focuses on designing borophene-based sensors with single-peak absorption, which lacks reliability compared to the performance of multi-peak sensors. In this paper, high-performance borophene refractive index sensors with multiple nearly perfect absorption peaks are proposed. The geometric parameters of the metadevices are optimized using finite-difference time-domain (FDTD) simulations to obtain three strong absorption peaks in the near-infrared (near-IR) regime with intensities of 99.72%, 99.18%, and 98.13%. Further calculations show that the sensitivities of the three strong peaks reach 339.1 nm shift per refractive index units (nm/RIU), 410.1 nm/RIU, and 738.1 nm/RIU, and their corresponding figure of merits (FOMs) reach up to 15.41/RIU, 14.65/RIU and 10.85/RIU, respectively, exhibiting great potential for near-IR refractive index sensing. Moreover, the three absorption peaks can be easily tuned by adjusting the carrier density of borophene, which can be realized by applying different external electric bias voltages. These results can provide theoretical guidance for the development of multi-peak near-IR dynamic integrated photonic devices

physics.optics↗

arXiv2022

Graph Neural Networks in Recommender Systems: A Survey

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.

cs.IR↗

arXiv2021

Molecular Understanding of the Effect of Hydrogen on Graphene Growth by Plasma-Enhanced Chemical Vapor Deposition

Plasma-enhanced chemical vapor deposition (PECVD) provides a low-temperature, highly-efficient, and catalyst-free route to fabricate graphene materials by virtue of the unique properties of plasma. In this paper, we conduct reactive molecular dynamics simulations to theoretically study the detailed growth process of graphene by PECVD at the atomic scale. Hydrocarbon radicals with different carbon/hydrogen (C/H) ratios are employed as dissociated precursors in the plasma environment during the growth process. The simulation results show that hydrogen content in the precursors significantly affects the growth behavior and critical properties of graphene. The highest number of hexagonal carbon rings formed in the graphene sheets, which is an indicator of their quality, is achieved for a C/H ratio of 1:1 in the precursors. Moreover, increasing the content of hydrogen in the precursors is shown to reduce the growth rate of carbon clusters, and prevent the formation of curved carbon structures during the growth process. The findings provide a detailed understanding of the fundamental mechanisms regarding the effects of hydrogen on the growth of graphene in a PECVD process.

physics.plasm-ph↗

arXiv2021

Enhanced Thermal Transport across the Interface between Charged Graphene Electrodes and Poly(ethylene oxide) Electrolytes by Non-covalent Functionalization

Interfacial thermal transport between electrodes and polymer electrolytes can play a crucial role in the thermal management of solid-state lithium-ion batteries (SLIBs). Modifying the electrode surface with functional molecules can effectively increase the interfacial thermal conductance (ITC) between electrodes and polymers (e.g., electrolytes, separators); however, how they influence the interfacial thermal transport in SLIBs during charge/discharge remains unknown. In this work, we conduct molecular dynamics (MD) simulations to investigate the ITC between charged electrodes and solid-state polymer electrolytes (SPEs) mixed with ionic liquids (ILs). We find that ILs could self assemble at the electrode surface and act as non-covalent functional molecules that could significantly enhance the interfacial thermal transport during charge/discharge because of the formation of a densely packed cationic or anionic layer at the interface. While the electrostatic interactions between the charged electrode and the IL ions are responsible for forming these dense interfacial layers, the enhancement of ITC is mainly contributed by the increased Lennard-Jones (LJ) interactions between the charged electrodes and ILs. This work may provide useful insights into the understanding of interfacial thermal transport between electrodes and electrolytes of SLIBs during charge/discharge.

cond-mat.mes-hall↗

arXiv2021

CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation

To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. To select these effective items, it is essential to estimate the causal effect of recommendations. The real effective items are the ones which can contribute to purchase probability uplift. Nevertheless, it is difficult to obtain the real causal effect since we can only recommend or not recommend an item to a user at one time. Furthermore, previous works usually rely on the randomized controlled trial~(RCT) experiment to evaluate their performance. However, it is usually not practicable in the recommendation scenario due to its unavailable time consuming. To tackle these problems, in this paper, we propose a causal collaborative filtering~(CausCF) method inspired by the widely adopted collaborative filtering~(CF) technique. It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations. CausCF extends the classical matrix factorization to the tensor factorization with three dimensions -- user, item, and treatment. Furthermore, we also employs regression discontinuity design (RDD) to evaluate the precision of the estimated causal effects from different models. With the testable assumptions, RDD analysis can provide an unbiased causal conclusion without RCT experiments. Through dedicated experiments on both the public datasets and the industrial application, we demonstrate the effectiveness of our proposed CausCF on the causal effect estimation and ranking performance improvement.

cs.IR↗

arXiv2021

Contrastive Learning for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimize the huge amounts of parameters. They usually suffer from the data sparsity problem, which makes it difficult for them to learn high-quality user representations. To tackle that, inspired by recent advances of contrastive learning techniques in the computer version, we propose a novel multi-task model called \textbf{C}ontrastive \textbf{L}earning for \textbf{S}equential \textbf{Rec}ommendation~(\textbf{CL4SRec}). CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. Therefore, it can extract more meaningful user patterns and further encode the user representation effectively. In addition, we propose three data augmentation approaches to construct self-supervision signals. Extensive experiments on four public datasets demonstrate that CL4SRec achieves state-of-the-art performance over existing baselines by inferring better user representations.

cs.IR↗