b'Nan Liu'

research

∙ 03/01/2023

FedScore: A privacy-preserving framework for federated scoring system development

We propose FedScore, a privacy-preserving federated learning framework f...

1 Siqi Li, et al. ∙

research

∙ 02/18/2023

Resource Allocation for Cell-free Massive MIMO-enabled URLLC Downlink Systems

Ultra-reliable and low-latency communication (URLLC) is a pivotal techni...

0 Qihao Peng, et al. ∙

research

∙ 01/29/2023

Discrete gradient structure of a second-order variable-step method for nonlinear integro-differential models

The discrete gradient structure and the positive definiteness of discret...

0 Hong-lin Liao, et al. ∙

research

∙ 12/16/2022

Shapley variable importance cloud for machine learning models

Current practice in interpretable machine learning often focuses on expl...

0 Yilin Ning, et al. ∙

research

∙ 12/16/2022

An ensemble neural network approach to forecast Dengue outbreak based on climatic condition

Dengue fever is a virulent disease spreading over 100 tropical and subtr...

0 Madhurima Panja, et al. ∙

research

∙ 11/22/2022

Resource Allocation for Uplink Cell-Free Massive MIMO enabled URLLC in a Smart Factory

Smart factories need to support the simultaneous communication of multip...

0 Qihao Peng, et al. ∙

research

∙ 10/22/2022

Asymptotically compatible energy of variable-step fractional BDF2 formula for time-fractional Cahn-Hilliard model

A new discrete energy dissipation law of the variable-step fractional BD...

0 Hong-lin Liao, et al. ∙

research

∙ 10/15/2022

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

Objective: The proper handling of missing values is critical to deliveri...

0 Mingxuan Liu, et al. ∙

research

∙ 06/21/2022

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

Infectious diseases remain among the top contributors to human illness a...

4 Madhurima Panja, et al. ∙

research

∙ 06/08/2022

Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making

Objective: Shapley additive explanations (SHAP) is a popular post-hoc te...

0 Mingxuan Liu, et al. ∙

research

∙ 06/06/2022

Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM

In this paper, we focus our attention on private Empirical Risk Minimiza...

0 Yuzhe Li, et al. ∙

research

∙ 06/03/2022

Compositional Visual Generation with Composable Diffusion Models

Large text-guided diffusion models, such as DALLE-2, are able to generat...

8 Nan Liu, et al. ∙

research

∙ 02/17/2022

AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes

Background: Risk prediction models are useful tools in clinical decision...

0 Seyed Ehsan Saffari, et al. ∙

research

∙ 01/16/2022

Unconditionally optimal error estimate of a linearized variable-time-step BDF2 scheme for nonlinear parabolic equations

In this paper we consider a linearized variable-time-step two-step backw...

0 Chengchao Zhao, et al. ∙

research

∙ 01/10/2022

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort

Risk scores are widely used for clinical decision making and commonly ge...

7 Yilin Ning, et al. ∙

research

∙ 11/22/2021

Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records

There is a continuously growing demand for emergency department (ED) ser...

13 Feng Xie, et al. ∙

research

∙ 11/18/2021

HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis

Objective: Heart rate variability (HRV) has been proven to be an importa...

0 Chenglin Niu, et al. ∙

research

∙ 11/17/2021

Learning to Compose Visual Relations

The visual world around us can be described as a structured set of objec...

22 Nan Liu, et al. ∙

research

∙ 10/06/2021

Shapley variable importance clouds for interpretable machine learning

Interpretable machine learning has been focusing on explaining final mod...

0 Yilin Ning, et al. ∙

research

∙ 09/13/2021

Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

A critical aspect of autonomous vehicles (AVs) is the object detection s...

0 Won Park, et al. ∙

research

∙ 07/21/2021

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Objective: Temporal electronic health records (EHRs) can be a wealth of ...

4 Feng Xie, et al. ∙

research

∙ 07/13/2021

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

Background: Medical decision-making impacts both individual and public h...

6 Han Yuan, et al. ∙

research

∙ 06/13/2021

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

Scoring systems are highly interpretable and widely used to evaluate tim...

6 Feng Xie, et al. ∙

research

∙ 04/09/2021

Fill-in-the-blank as a Challenging Video Understanding Evaluation Framework

Work to date on language-informed video understanding has primarily addr...

1 Santiago Castro, et al. ∙

research

∙ 12/07/2020

Cluster analysis of presolar silicon carbide grains: evaluation of their classification and astrophysical implications

Cluster analysis of presolar silicon carbide grains based on literature ...

0 Asmaa Boujibar, et al. ∙

research

∙ 10/16/2020

The Capacity of Symmetric Private Information Retrieval under Arbitrary Collusion and Eavesdropping Patterns

We study the symmetric private information retrieval (SPIR) problem unde...

0 Jiale Cheng, et al. ∙

research

∙ 04/02/2020

A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

Federated learning has been widely studied and applied to various scenar...

0 Yuzheng Li, et al. ∙

research

∙ 01/12/2020

The Capacity of Private Information Retrieval Under Arbitrary Collusion Patterns

We study the private information retrieval (PIR) problem under arbitrary...

0 Xinyu Yao, et al. ∙

research

∙ 05/03/2019

Time-sync Video Tag Extraction Using Semantic Association Graph

Time-sync comments reveal a new way of extracting the online video tags....

0 Wenmian Yang, et al. ∙

research

∙ 01/21/2019

The Capacity of Multi-round Private Information Retrieval from Byzantine Databases

In this work, we investigate the capacity of private information retriev...

0 Xinyu Yao, et al. ∙

research

∙ 06/29/2018

The Economics of Video Websites with Membership-Advertising Mode in Wireless Networks

In this paper, we consider a novel business model of video websites via ...

0 Qing Zhou, et al. ∙

research

∙ 02/08/2018

Coded Caching with Heterogeneous Cache Sizes and Link Qualities: The Two-User Case

The centralized coded caching problem is studied under heterogeneous cac...

0 Daming Cao, et al. ∙

research

∙ 06/14/2017

Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition

Webly-supervised learning has recently emerged as an alternative paradig...

0 Christian Rupprecht, et al. ∙

Nan Liu

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