Search Results for author: Shu Wang

Found 20 papers, 7 papers with code

A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph

no code implementations29 Oct 2023 Zhihang Yu, Shu Wang, Yunqiang Zhu, Zhiqiang Zou

However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context.

Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks

no code implementations13 Oct 2023 Harsh Patel, Yuan Zhou, Alexander P Lamb, Shu Wang, Jieliang Luo

By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.

Reinforcement Learning (RL) Scheduling

Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations

no code implementations21 Sep 2023 Zhihang Yu, Shu Wang, Yunqiang Zhu, Wen Yuan, Xiaoliang Dai, Zhiqiang Zou

However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns.

Asymmetric Patch Sampling for Contrastive Learning

1 code implementation5 Jun 2023 Chengchao Shen, Jianzhong Chen, Shu Wang, Hulin Kuang, Jin Liu, Jianxin Wang

Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning.

Contrastive Learning Instance Segmentation +3

T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events

no code implementations7 Feb 2022 Shu Wang, Yuhuang Hu, Shih-Chii Liu

This work proposes a self-supervised method called Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network pretrained on spectrogram features so that the network works with the cochlea event features.

speech-recognition Speech Recognition

Evolved Massive Stars at Low-metallicity IV. Using 1.6 $μ$m "H-bump" to identify red supergiant stars: a case study of NGC 6822

no code implementations21 Jan 2021 Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Man I Lam, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri

Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates.

Solar and Stellar Astrophysics Astrophysics of Galaxies

Multi Receptive Field Network for Semantic Segmentation

no code implementations17 Nov 2020 Jianlong Yuan, Zelu Deng, Shu Wang, Zhenbo Luo

Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image.

Ranked #16 on Semantic Segmentation on Cityscapes test (using extra training data)

Segmentation Semantic Segmentation

The Bright Side and the Dark Side of Hybrid Organic Inorganic Perovskites

no code implementations23 Oct 2020 Wladek Walukiewicz, Shu Wang, Xinchun Wu, Rundong Li, Matthew P. Sherburne, Bo Wu, Tze Chien Sun, Joel W. Ager, Mark D. Asta

The previously developed bistable amphoteric native defect (BAND) model is used for a comprehensive explanation of the unique photophysical properties and for understanding the remarkable performance of perovskites as photovoltaic materials.

Applied Physics Materials Science

Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

no code implementations24 Jul 2020 Xiaofeng Gao, Ran Gong, Yizhou Zhao, Shu Wang, Tianmin Shu, Song-Chun Zhu

Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.

Bayesian Inference Explainable Artificial Intelligence (XAI) +1

A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables

no code implementations9 May 2019 Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang

To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results.

Clustering Variable Selection

Hybrid Density- and Partition-based Clustering Algorithm for Data with Mixed-type Variables

no code implementations6 May 2019 Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang

However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, despite the abundance of data with mixed types particularly in the medical field.


VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning

1 code implementation13 Mar 2019 Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, Song-Chun Zhu

One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.

reinforcement-learning Reinforcement Learning (RL)

Differentially Private Generative Adversarial Network

2 code implementations19 Feb 2018 Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou

Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models.

Collaborative Deep Reinforcement Learning

1 code implementation19 Feb 2017 Kaixiang Lin, Shu Wang, Jiayu Zhou

Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents.

Knowledge Distillation OpenAI Gym +3

An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

no code implementations4 Jan 2017 Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan, Shu Wang, Bin Hu, Li Cheng, David S. Rosenblum

We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.

Activity Recognition

Multispectral Deep Neural Networks for Pedestrian Detection

2 code implementations8 Nov 2016 Jingjing Liu, Shaoting Zhang, Shu Wang, Dimitris N. Metaxas

Multispectral pedestrian detection is essential for around-the-clock applications, e. g., surveillance and autonomous driving.

Pedestrian Detection

Visual Tracking via Reliable Memories

no code implementations4 Feb 2016 Shu Wang, Shaoting Zhang, Wei Liu, Dimitris N. Metaxas

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks.

Clustering Visual Tracking

A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling

no code implementations LREC 2014 Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, Dimitris Metaxas

Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success.

3D Reconstruction Sign Language Recognition +1

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