Xu Zhang

Real-time Attention Based Look-alike Model for Recommender System

2019

Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more …

Controllable Switching between Superradiant and Subradiant States in a 10-qubit Superconducting Circuit

2020 · Physical Review Letters

Superradiance and subradiance concerning enhanced and inhibited collective radiation of an ensemble of atoms have been a central topic in quantum optics. However, precise generation and control of these states remain challenging. Here we deterministically …

Predicting Online News Authorship by an Authorship Embeddings Space Method

2020

In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification …

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

2021

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system …

Selective Fairness in Recommendation via Prompts

2022 · arXiv (Cornell University)

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which …

Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?

2023 · ACM Transactions on Software Engineering and Methodology

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics …

Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling

2023 · arXiv (Cornell University)

The NL2SQL task involves parsing natural language statements into SQL queries. While most state-of-the-art methods treat NL2SQL as a slot-filling task and use feature representation learning techniques, they overlook explicit correlation features between the SELECT …

Evaluating Long-Context Understanding via Latent and Positional Structure Queries in Large Language Models

2024

The ability to handle long-context dependencies is a critical challenge for modern language models, especially in tasks that require the retention of information over extended input sequences. Differentiating between latent and positional queries introduces a …

Enabling Virtual Priority in Data Center Congestion Control

2025

In data center networks, various types of traffic with strict performance requirements operate simultaneously, necessitating effective isolation and scheduling through priority queues. However, most switches support only around ten priority queues. Virtual priority can address …

Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference

2025 · arXiv (Cornell University)

In recent years, compressed domain semantic inference has primarily relied on learned image coding models optimized for mean squared error (MSE). However, MSE-oriented optimization tends to yield latent spaces with limited semantic richness, which hinders …