Yong Li

Classical dynamical localization in a strongly driven two-mode mechanical system

2017 · arXiv (Cornell University)

We report the realization of dynamical localization in a strongly driven two-mode optomechanical system consisting of two coupled cantilevers. Due to the coupling, mechanical oscillations can transport between the cantilevers. However, by placing one of …

Beyond K-Anonymity: Protect Your Trajectory from Semantic Attack

2017

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works are dedicated to merging …

Recommender Systems with Characterized Social Regularization

2018

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users …

Enantio-discrimination via the cavity-assisted three-photon process

2021 · arXiv (Cornell University)

We propose a theoretical method for enantio-discrimination of chiral molecules based on a cavity-molecules system, which consists of a cavity without external driving and an ensemble of chiral mixture (containing left- and right- handed molecules) …

Neighboring Backdoor Attacks on Graph Convolutional Network

2022 · arXiv (Cornell University)

Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success …

Redactable Blockchain with K-Time Controllable Editing

2022

Considering the immutability of traditional blockchain restricts the governance and regulation of sensitive data on chain, this paper proposes a redactable blockchain scheme with k-time controllable cheating editing (CCE-RB for short). In the CCE-RB scheme, …

A-BBL: A Risk Prediction Model for Patient Readmission based on Electronic Medical Records

2023 · Journal of Computing and Electronic Information Management

With the spread of medical digitization, electronic health record data has been accumulated in large quantities, laying the foundation for intelligent medical changes. ICU data is mined and analyzed to identify the risk of patient …

Mixed Attention Network for Cross-domain Sequential Recommendation

2023 · arXiv (Cornell University)

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which …

Llumnix: Dynamic Scheduling for Large Language Model Serving

2024 · arXiv (Cornell University)

Inference serving for large language models (LLMs) is the key to unleashing their potential in people's daily lives. However, efficient LLM serving remains challenging today because the requests are inherently heterogeneous and unpredictable in terms …

Efficient shortest distance approximate query on large scale encrypted graph data

2025 · Cybersecurity

Abstract The problem of querying shortest distance on a graph has attracted significant research attention due to the widespread applicability of graphs and the ability of graph shortest path queries to address numerous application problems. …

Probing Neural Topology of Large Language Models

2025 · arXiv (Cornell University)

Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. However, the complex mechanisms that link neuron's functional co-activation with the emergent model capabilities remains …

Physical-Informed Detailed Aggregation of Wind Farm Based on Symbolic Regression: Structure, Solution and Generalizability

2025 · IEEE Transactions on Sustainable Energy

Detailed aggregation of wind farms can be beneficial for efficiently simulating and analyzing the dynamic interactional behaviors with power systems. However, methods like weighted aggregation and parameter identification are inefficient and inaccurate in depicting simultaneously …

Route-and-Reason: Scaling Large Language Model Reasoning with Reinforced Model Router

2025 · arXiv (Cornell University)

Chain-of-thought has been proven essential for enhancing the complex reasoning abilities of Large Language Models (LLMs), but it also leads to high computational costs. Recent advances have explored the method to route queries among multiple …

Structural controls on the Dadonggou gold deposit in Liaodong Peninsula, East China

2026 · Ore Geology Reviews

• Dadonggou is a giant gold deposit hosted by Paleoproterozoic phyllite. • Gold orebodies are mainly controlled by subhorizontal to gently dipping structures formed during Early Cretaceous detachment faulting. • Ore fluids migrated through linked …

Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

2019 · IEEE Transactions on Knowledge and Data Engineering

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we …

Reinforced Negative Sampling for Recommendation with Exposure Data

2019

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we …

Multi-behavior Recommendation with Graph Convolutional Networks

2020

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks …

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

2021

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, …

Sequential Recommendation with Graph Neural Networks

2021

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often …

Graph Neural Networks for Recommender System

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse …

Disentangling Long and Short-Term Interests for Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may …

A Review-aware Graph Contrastive Learning Framework for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing …

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

2023 · ACM Transactions on Recommender Systems

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of …