Search Results for author: Jingbo Zhou

Found 32 papers, 12 papers with code

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

no code implementations21 Oct 2023 Lihang Liu, Donglong He, Xianbin Ye, Jingbo Zhou, Shanzhuo Zhang, Xiaonan Zhang, Jun Li, Hua Chai, Fan Wang, Jingzhou He, Liang Zheng, Yonghui Li, Xiaomin Fang

In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance.

Drug Discovery Molecular Docking

LLM for SoC Security: A Paradigm Shift

no code implementations9 Oct 2023 Dipayan Saha, Shams Tarek, Katayoon Yahyaei, Sujan Kumar Saha, Jingbo Zhou, Mark Tehranipoor, Farimah Farahmandi

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges.

Natural Language Understanding Program Synthesis

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

no code implementations31 Aug 2023 Weijia Zhang, Le Zhang, Jindong Han, Hao liu, Jingbo Zhou, Yu Mei, Hui Xiong

Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.

Time Series Time Series Forecasting

Tuning Pre-trained Model via Moment Probing

1 code implementation ICCV 2023 Mingze Gao, Qilong Wang, Zhenyi Lin, Pengfei Zhu, QinGhua Hu, Jingbo Zhou

Distinguished from LP which builds a linear classification head based on the mean of final features (e. g., word tokens for ViT) or classification tokens, our MP performs a linear classifier on feature distribution, which provides the stronger representation ability by exploiting richer statistical information inherent in features.

Image Classification

Spatial Heterophily Aware Graph Neural Networks

1 code implementation21 Jun 2023 Congxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu, Hui Xiong

However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity.

Multi-Temporal Relationship Inference in Urban Areas

1 code implementation15 Jun 2023 Shuangli Li, Jingbo Zhou, Ji Liu, Tong Xu, Enhong Chen, Hui Xiong

Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).

Graph Learning Representation Learning +1

Deep Graph Neural Networks via Flexible Subgraph Aggregation

no code implementations9 May 2023 Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Di Jin, Carl Yang, Rui Zhang

Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution.

Adaptive Depth Graph Attention Networks

no code implementations16 Jan 2023 Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Rui Zhang

As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results.

Graph Attention Graph Mining

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

1 code implementation5 Jan 2023 Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Jingbo Zhou, Dejing Dou, Hui Xiong

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.

Descriptive Language Modelling +1

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

no code implementations1 Dec 2022 Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, Yanjie Fu

The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.

A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection

no code implementations26 Nov 2022 Congxi Xiao, Jingbo Zhou, Jizhou Huang, HengShu Zhu, Tong Xu, Dejing Dou, Hui Xiong

The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.


Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

no code implementations24 Nov 2022 Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou

The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process.

Bayesian Optimization Fairness +2

Robust Training of Graph Neural Networks via Noise Governance

1 code implementation12 Nov 2022 Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z. Chen, Jian Wu

To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.


SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

1 code implementation8 Aug 2022 Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, Dejing Dou

Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation.

Structure-aware Protein Self-supervised Learning

1 code implementation6 Apr 2022 Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, Dejing Dou

Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.

Protein Language Model Representation Learning +1

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

1 code implementation24 Sep 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong

Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.

Contrastive Learning Data Augmentation +4

Adversarial Neural Trip Recommendation

no code implementations24 Sep 2021 Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, Jizhou Huang, Hui Xiong

To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.

Recommendation Systems

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

1 code implementation21 Jul 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong

To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).

Drug Discovery Graph Attention +1

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

no code implementations8 Jan 2021 Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao liu, Hui Xiong

We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.

Multi-Task Learning Representation Learning

C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak

no code implementations22 Dec 2020 Congxi Xiao, Jingbo Zhou, Jizhou Huang, An Zhuo, Ji Liu, Haoyi Xiong, Dejing Dou

Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city.

Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction

1 code implementation17 Dec 2020 Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu, Hui Xiong, Dejing Dou

The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.

Drug Discovery Graph Attention +1

Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection

no code implementations26 Aug 2020 Dongjie Wang, Pengyang Wang, Jingbo Zhou, Leilei Sun, Bowen Du, Yanjie Fu

To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.

Anomaly Detection Cyber Attack Detection +1

Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine

no code implementations11 Jul 2020 Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Jingbo Zhou, Xu Ma, Hui Xiong

Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

1 code implementation24 Nov 2019 Weijia Zhang, Hao liu, Yanchi Liu, Jingbo Zhou, Hui Xiong

However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).


Store Location Selection via Mining Search Query Logs of Baidu Maps

no code implementations12 Jun 2016 Mengwen Xu, Tianyi Wang, Zhengwei Wu, Jingbo Zhou, Jian Li, Haishan Wu

In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps.


A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report

1 code implementation28 Mar 2016 Jingbo Zhou, Qi Guo, H. V. Jagadish, Luboš Krčál, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.

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