Yan Zhang

Transfer Reinforcement Learning under Unobserved Contextual Information

2020

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy …

Liver Tumor Image Enhancement and CDK1 Gene Mutation Prediction Method

2020

Liver cancer is one of the most common malignancies, which has extremely high mortality rate. Gene sequencing can reveal genetic variants of hepatocytes. The CDK1 gene has the potential to target anti-tumor. Therefore, the prediction …

Disentangle-based Continual Graph Representation Learning

2020 · arXiv (Cornell University)

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since …

Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 Pandemic: A Blockchain Perspective

2020 · IEEE Transactions on Network Science and Engineering

Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical …

News Crawling Based on Python Crawler

2021 · Journal of Physics Conference Series

Abstract News is an important form to reflect current politics, which attracts people’s attention. The emergence of crawlers provides a convenient way for people to obtain useful information from mass news. Through Python language, this …

Visualization for Dichotomous Variables, the Independence and Markov chains

2021 · arXiv (Cornell University)

In probability theory, the independence is a very fundamental concept, but with a little mystery. People can always easily manipulate it logistically but not geometrically, especially when it comes to the independence relationships among more …

Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

2022 · Journal of New Media

In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just …

Influences of Digital Twin Technology on Learning Effect

2022 · Journal of Engineering Science and Technology Review

This study designed a questionnaire to investigate the influences of digital twin (DT) technology on ubiquitous learning effect based on the relevant theory. The influences of four subsystems (virtual-reality symbiotic system, information transmission system, intelligent …

Formal Verification of Stochastic Systems with ReLU Neural Network Controllers

2021 · arXiv (Cornell University)

In this work, we address the problem of formal safety verification for stochastic cyber-physical systems (CPS) equipped with ReLU neural network (NN) controllers. Our goal is to find the set of initial states from where, …

Research on Internet of things control system based on EEG signal

2022 · 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

The brain computer interface technology using EEG signals is widely used in the fields of emotion recognition, state monitoring, medical rehabilitation, etc. The brain computer interface technology which "builds a bridge between the human brain …

CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification

2023

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one …

Selective Kernel Fusion Complex-Valued CNN for Modulation Recognition

2023

Automatic modulation recognition (AMR) plays an essential role in intelligent communication networks monitoring, management, and optimization. Recently, it has been shown that deep learning-based methods perform well in AMR. However, most existing methods are based …

Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset

2023

Imbalanced data, a common occurrence in real-world datasets, presents a challenge for machine learning classification models. These models are typically designed with the assumption of balanced class distributions, leading to lower predictive performance when faced …

Semi-Supervised Disease Classification Based on Limited Medical Image Data

2024 · IEEE Journal of Biomedical and Health Informatics

Inrecent years, significant progress has been made in the field of learning from positive and unlabeled examples (PU learning), particularly in the context of advancing image and text classification tasks. However, applying PU learning to …

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models

2024 · arXiv (Cornell University)

The strong capability of large language models (LLMs) has been applied to information extraction (IE) through either retrieval augmented prompting or instruction tuning (IT). However, the best way to incorporate information with LLMs for IE …

Smart Roads: Roadside Perception, Vehicle-Road Cooperation, and Business Model

2024 · IEEE Network

Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time …

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection

2024 · arXiv (Cornell University)

Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD methods face a common challenge: the inherent bias in normal samples, which causes …

N3C: Towards Replay-based Novelty Continual Clustering with Class-Overlapping

2025

Deep clustering has excelled in batch settings, but little work has addressed the more practical and challenging continual clustering (CC) with shifting data distributions. Additionally, class-overlapping, also a challenging issue, where classes recur across tasks, …

An Edge-Turning Strategy Based on Greedy Algorithm to Optimize the Network Controllability

2025

Structural optimization is a significant problem in the research of complex network controllability. Conventional approaches based on global optimization may often not distinguish the role of local network in structural optimization. This paper suggests an …

On a surprising behavior of the likelihood ratio test in non-parametric mixture models

2025 · arXiv (Cornell University)

We study the likelihood ratio test in general mixture models where the base density is parametric, the null is a known fixed mixing distribution, and the alternative is a general mixing distribution supported on a …

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

2019 · Transactions of the Association for Computational Linguistics

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional …

Attention Guided Graph Convolutional Networks for Relation Extraction

2019

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains …

PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

2021 · arXiv (Cornell University)

Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demonstrated strong performances on natural language understanding and generation with …