Zheng Liu

Kernel-based Gaussian process for anomaly detection in sparse gamma-ray data

2020 · PLoS ONE

In radioactive source surveying protocols, a number of task-inherent features degrade the quality of collected gamma ray spectra, including: limited dwell times, a fluctuating background, a large distance to the source, weak source activity, and …

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study

2021 · PLoS ONE

Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a …

A Combination Method of Resampling and Random Forest for Imbalanced Data Classification

2022 · 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)

In the research of imbalanced data classification, the resampling effect of the existing resampling and random forest combination technology is greatly affected by the characteristic dimension of the training set, resulting in the unsatisfactory classification …

An ATR Method for Imbalanced Data SAR Images Based on CCEGAN

2023

Synthetic aperture radar (SAR) automatic target recognition (ATR) is an important application of SAR. In military applications, it is unrealistic to be able to acquire balanced SAR target images due to the non-cooperative nature of …

Robust Kalman Predictor under Linearly Correlated Noise and Mixed Uncertainties of Noise Variances and Multiple Networked-inducements

2024

The paper solves the robust Kalman prediction problem for systems with linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises and multiple networked-inducements including missing measurements, packets dropouts and two-step random measurement delays. …

AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark

2024 · arXiv (Cornell University)

Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data, face limitations in addressing evaluation needs for emerging domains both …

Reinforced Information Retrieval

2025 · arXiv (Cornell University)

While retrieval techniques are widely used in practice, they still face significant challenges in cross-domain scenarios. Recently, generation-augmented methods have emerged as a promising solution to this problem. These methods enhance raw queries by incorporating …

Towards Effective Code-Integrated Reasoning

2025 · arXiv (Cornell University)

In this paper, we investigate code-integrated reasoning, where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire this capability, models must learn when and how to use …

ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval

2026

In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval.Our work includes three key technical contributions.First, we propose ReMixer, a new data synthesis method that overcomes the triviality problem …

TASE: Token Awareness and Structured Evaluation for Multilingual Language Models

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

While large language models (LLMs) have demonstrated remarkable performance on high-level semantic tasks, they often struggle with fine-grained, token-level understanding and structural reasoning—capabilities that are essential for applications requiring precision and control. We introduce TASE, …

Neural News Recommendation with Long- and Short-term User Representations

2019

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both …

Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

2020

Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization …

Fine-grained Interest Matching for Neural News Recommendation

2020

Personalized news recommendation is a critical technology to improve users' online news reading experience. The core of news recommendation is accurate matching between user's interests and candidate news. The same user usually has diverse interests …