Search Results for author: Yu Fu

Found 30 papers, 7 papers with code

Cross-Task Defense: Instruction-Tuning LLMs for Content Safety

1 code implementation24 May 2024 Yu Fu, Wen Xiao, Jia Chen, Jiachen Li, Evangelos Papalexakis, Aichi Chien, Yue Dong

Recent studies reveal that Large Language Models (LLMs) face challenges in balancing safety with utility, particularly when processing long texts for NLP tasks like summarization and translation.

CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation

no code implementations26 Mar 2024 Yongrui Yu, HanYu Chen, Zitian Zhang, Qiong Xiao, Wenhui Lei, Linrui Dai, Yu Fu, Hui Tan, Guan Wang, Peng Gao, Xiaofan Zhang

To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data.

Denoising Image Generation +4

Understanding Cellular Noise with Optical Perturbation and Deep Learning

no code implementations23 Jan 2024 Chuanbo Liu, Yu Fu, Lu Lin, Elliot L. Elson, Jin Wang

This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.

Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA

no code implementations20 Jan 2024 Xi Chen, MingKe You, Li Wang, Weizhi Liu, Yu Fu, Jie Xu, Shaoting Zhang, Gang Chen, Kang Li, Jian Li

This study focused on evaluating and enhancing the clinical capabilities of LLMs in specific domains, using osteoarthritis (OA) management as a case study.

Management Retrieval

Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack

1 code implementation12 Dec 2023 Yu Fu, Yufei Li, Wen Xiao, Cong Liu, Yue Dong

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration?

Question Answering

Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

no code implementations16 Oct 2023 Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, Pedram Zaree, Yue Dong, Nael Abu-Ghazaleh

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows.

Adversarial Attack Federated Learning

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

1 code implementation ICCV 2023 Yanyan Huang, Weiqin Zhao, Shujun Wang, Yu Fu, Yuming Jiang, Lequan Yu

In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets.

Continual Learning

Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy

1 code implementation25 Jul 2023 Yu Fu, Deyi Xiong, Yue Dong

To mitigate potential risks associated with language models, recent AI detection research proposes incorporating watermarks into machine-generated text through random vocabulary restrictions and utilizing this information for detection.

Conditional Text Generation Data-to-Text Generation

SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size

no code implementations30 May 2023 Yu Fu, Yanyan Huang, Shunjie Dong, Yalin Wang, Tianbai Yu, Meng Niu, Cheng Zhuo

Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders.

Age Estimation

Multi-view Spectral Polarization Propagation for Video Glass Segmentation

no code implementations ICCV 2023 Yu Qiao, Bo Dong, Ao Jin, Yu Fu, Seung-Hwan Baek, Felix Heide, Pieter Peers, Xiaopeng Wei, Xin Yang

In this paper, we present the first polarization-guided video glass segmentation propagation solution (PGVS-Net) that can robustly and coherently propagate glass segmentation in RGB-P video sequences.

Image Segmentation Segmentation +1

Inverse Reinforcement Learning for Text Summarization

no code implementations19 Dec 2022 Yu Fu, Deyi Xiong, Yue Dong

We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors.

Abstractive Text Summarization reinforcement-learning +1

Scene Graph Modification as Incremental Structure Expanding

no code implementations COLING 2022 Xuming Hu, Zhijiang Guo, Yu Fu, Lijie Wen, Philip S. Yu

A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.

OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt

2 code implementations10 May 2022 Yu Fu, Yanyan Huang, Yalin Wang, Shunjie Dong, Le Xue, Xunzhao Yin, Qianqian Yang, Yiyu Shi, Cheng Zhuo

In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.

Age Estimation

Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature

no code implementations8 May 2022 Xueyuan Duan, Yu Fu, Kun Wang

To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is proposed.

Anomaly Detection Traffic Classification

Activate index: an integrated index to reveal disrupted brain network organizations of major depressive disorder patients

no code implementations14 Feb 2022 Yu Fu, Yanyan Huang, Meng Niu, Le Xue, Shunjie Dong, Shunlin Guo, Junqiang Lei, Cheng Zhuo

This study for the first time discussed the differences between MDD and HC using both rich club and diverse club metrics and found the complementarity of them in analyzing brain networks.

A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis

no code implementations14 Feb 2022 Yu Fu, Shunjie Dong, Yi Liao, Le Xue, Yuanfan Xu, Feng Li, Qianqian Yang, Tianbai Yu, Mei Tian, Cheng Zhuo

18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients.

Generative Adversarial Network Image Reconstruction

Task Decoupled Framework for Reference-Based Super-Resolution

no code implementations CVPR 2022 Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Ya zhang, Yan-Feng Wang, Dazhi He

Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.

Image Super-Resolution Reference-based Super-Resolution

PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion

no code implementations29 Jul 2021 Yu Fu, Tianyang Xu, XiaoJun Wu, Josef Kittler

In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations.

Image Classification Image Reconstruction

RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection

no code implementations22 Feb 2021 Shunjie Dong, Qianqian Yang, Yu Fu, Mei Tian, Cheng Zhuo

The novel 2019 Coronavirus (COVID-19) infection has spread world widely and is currently a major healthcare challenge around the world.

Computed Tomography (CT)

BER Performance of Spatial Modulation Systems Under a Non-Stationary Massive MIMO Channel Model

no code implementations28 Jul 2020 Yu Fu, Cheng-Xiang Wang, Xuming Fang, Li Yan, Stephen McLaughlin

When compared with the V-BLAST system and the channel inversion system, SM approaches offer advantages in performance for MU massive MIMO systems.

End-to-End Energy Efficiency Evaluation for B5G Ultra Dense Networks

no code implementations28 Jul 2020 Yu Fu, Mohammad Dehghani Soltani, Hamada Alshaer, Cheng-Xiang Wang, Majid Safari, Stephen McLaughlin, Harald Haas

This paper proposes an end-to-end (e2e) power consumption model and studies the energy efficiency for a heterogeneous B5G cellular architecture that separates the indoor and outdoor communication scenarios in ultra dense networks.

Pan-Cancer Computational Histopathology (PC-CHiP) analysis using deep learning

1 code implementation27 Jul 2020 Yu Fu, Alexander W Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Artem Shmatko, Lucy Yates, Mercedes Jimenez-Linan, Luiza Moore, Moritz Gerstung

These findings demonstrate the large potential of computer vision to characterise the molecular basis of tumour histopathology and lay out a rationale for integrating molecular and histopathological data to augment diagnostic and prognostic workflows.

Transfer Learning

Spectrum-Energy-Economy Efficiency Trade-off of Wireless Communication Systems with Separated Indoor/Outdoor Scenarios for 5G and B5G

no code implementations23 Dec 2019 Yu Fu, Cheng-Xiang Wang, Zijun Zhao, Stephen McLaughlin

In this paper, we consider a heterogeneous 5G cellular architecture that separates the outdoor and indoor scenarios and in particular study the trade-off between the spectrum efficiency (SE), energy efficiency (EE), economy efficiency (ECE).

Optimizing seed inputs in fuzzing with machine learning

no code implementations7 Feb 2019 Liang Cheng, Yang Zhang, Yi Zhang, Chen Wu, Zhangtan Li, Yu Fu, Haisheng Li

Our experiments on a set of widely used PDF viewers demonstrate that the improved seed inputs produced by our framework could significantly increase the code coverage of the target program and the likelihood of detecting program crashes.

Cryptography and Security

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