Search Results for author: Fengli Xu

Found 12 papers, 3 papers with code

Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network

no code implementations18 Feb 2024 Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui

We propose a novel REasoning meta-STRUCTure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure.

Language Modelling Large Language Model +1

Beyond Imitation: Generating Human Mobility from Context-aware Reasoning with Large Language Models

no code implementations15 Feb 2024 Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding, Yuan Yuan, Meng Wang, Yong Li

In this paper, we design a novel Mobility Generation as Reasoning (MobiGeaR) framework that prompts LLM to recursively generate mobility behaviour.

In-Context Learning

DefInt: A Default-interventionist Framework for Efficient Reasoning with Hybrid Large Language Models

no code implementations4 Feb 2024 Yu Shang, Yu Li, Fengli Xu, Yong Li

Previous works like chain-of-thought (CoT) and tree-of-thoughts(ToT) have predominately focused on enhancing accuracy, but overlook the rapidly increasing token cost, which could be particularly problematic for open-ended real-world tasks with huge solution spaces.

Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment

1 code implementation19 Dec 2023 Fengli Xu, Jun Zhang, Chen Gao, Jie Feng, Yong Li

Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.

How enlightened self-interest guided global vaccine sharing benefits all: a modelling study

no code implementations13 Oct 2023 Zhenyu Han, Qianyue Hao, Qiwei He, Katherine Budeski, Depeng Jin, Fengli Xu, Kun Tang

We explore the possibility of the enlightened self-interest incentive mechanism, i. e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model.

Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities

no code implementations21 Feb 2021 Zhenyu Han, Fengli Xu, Yong Li, Tao Jiang, Depeng Jin, Jianhua Lu, James A. Evans

With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment.

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

1 code implementation21 Feb 2021 Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, Yong Li

To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs.

Recommendation Systems

AttnMove: History Enhanced Trajectory Recovery via Attentional Network

no code implementations3 Jan 2021 Tong Xia, Yunhan Qi, Jie Feng, Fengli Xu, Funing Sun, Diansheng Guo, Yong Li

A considerable amount of mobility data has been accumulated due to the proliferation of location-based service.

Automorphic Equivalence-aware Graph Neural Network

1 code implementation NeurIPS 2021 Fengli Xu, Quanming Yao, Pan Hui, Yong Li

Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e. g., computational biologist and social network analysis.

Representation Learning

Smartphone App Usage Prediction Using Points of Interest

no code implementations26 Nov 2017 Donghan Yu, Yong Li, Fengli Xu, Pengyu Zhang, Vassilis Kostakos

In this paper we present the first population-level, city-scale analysis of application usage on smartphones.

Transfer Learning

Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data

no code implementations21 Feb 2017 Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiao-Ming Fu, Depeng Jin

By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets.

Computers and Society Cryptography and Security

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