Publications (45)
Multi-Scale Transformer Architecture for Accurate Medical Image Classification
Jiacheng Hu, Yanlin Xiang, Yang Lin +3
This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medic…
Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation
Houze Liu, Bo Zhang, Yanlin Xiang +3
Recent advancements in artificial intelligence (AI) have precipitated a paradigm shift in medical imaging, particularly revolutionizing the domain of brain imaging. This paper syst…
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
Weijie He, Tong Zhou, Yanlin Xiang +3
This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evaluate its application effect in pn…
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Yang Lin, Tianyu Zhang, Peiqin Sun +2
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been deve…
Progress and summary of reinforcement learning on energy management of MPS-EV
Jincheng Hu, Yang Lin, Liang Chu +4
The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promisi…
LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization
Suchen Liu, Jun Gao, Yinjun Han +1
Query optimization is essential for efficient SQL query execution in DBMS, and remains attractive over time due to the growth of data volumes and advances in hardware. Existing tra…
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads
Hanlin Tang, Yang Lin, Jing Lin +4
The memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this…
Stability and Sample-based Approximations of Composite Stochastic Optimization Problems
Darinka Dentcheva, Yang Lin, Spiridon Penev
Optimization under uncertainty and risk is indispensable in many practical situations. Our paper addresses stability of optimization problems using composite risk functionals which…
Bias Reduction in Sample-Based Optimization
Darinka Dentcheva, Yang Lin
We consider stochastic optimization problems which use observed data to estimate essential characteristics of the random quantities involved. Sample average approximation (SAA) or…
Transporter: A 1284 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing
Yang Lin, Claudio Bruschini, Edoardo Charbon
Single-photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and…
Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real-time Fluorescence Lifetime Imaging
Yang Lin, Paul Mos, Andrei Ardelean +2
Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing…
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models
Islam Nassar, Yang Lin, Yuan Jin +8
Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for b…
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
Xinyu Ma, Xu Chu, Zhibang Yang +3
With the increasingly powerful performances and enormous scales of pretrained models, promoting parameter efficiency in fine-tuning has become a crucial need for effective and effi…
LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models
Weibin Liao, Xin Gao, Tianyu Jia +6
Natural Language to SQL (NL2SQL) aims to translate natural language queries into executable SQL statements, offering non-expert users intuitive access to databases. While recent ap…
A vision based system for underwater docking
Shuang Liu, Mete Ozay, Takayuki Okatani +3
Autonomous underwater vehicles (AUVs) have been deployed for underwater exploration. However, its potential is confined by its limited on-board battery energy and data storage capa…
Benchmarking Deep Models for Salient Object Detection
Huajun Zhou, Yang Lin, Lingxiao Yang +2
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task. However, the performance discrepancy ca…
Speaker Anonymisation for Speech-based Suicide Risk Detection
Ziyun Cui, Sike Jia, Yang Lin +6
Adolescent suicide is a critical global health issue, and speech provides a cost-effective modality for automatic suicide risk detection. Given the vulnerable population, protectin…
BoomHQ: Learning to Boost Multiple Hybrid Queries on Vector DBMSs
Ermu Qiu, Tianyi Chen, Jun Gao +4
Hybrid queries, which combine vector nearest neighbor searches with scalar predicates, represent a fundamental challenge in managing vector databases. Existing methods often restri…
Central limit theorems for vector-valued composite functionals with smoothing and applications
Huihui Chen, Darinka Dentcheva, Yang Lin +1
This paper focuses on vector-valued composite functionals, which may be nonlinear in probability. Our primary goal is to establish central limit theorems for these functionals when…
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning
Minghao Tang, Yongquan He, Yongxiu Xu +3
Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge kn…
Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
Xu Chu, Yang Lin, Jingyue Gao +3
Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Predicting the occurrence of DDIs helps drug safety professionals allocate investigative…
Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
Wu Fei, Hao Kong, Shuxian Liang +5
Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~(LLMs). However, introducing additiona…
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
Yang Lin, Xinyu Ma, Xu Chu +4
Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-serie…
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting
Yang Lin, Irena Koprinska, Mashud Rana
In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probab…
Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories
Tianlong Wang, Yuhang Wang, Weibin Liao +5
Current approaches to enhance Large Language Model (LLM) reasoning, such as Chain-of-Thought and "Wait" prompts, primarily encourage models to think more, yet often fail to guide t…
Progressive Neural Network for Multi-Horizon Time Series Forecasting
Yang Lin
In this paper, we introduce ProNet, an novel deep learning approach designed for multi-horizon time series forecasting, adaptively blending autoregressive (AR) and non-autoregressi…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Self-Organized Bosonic Domain Walls
Xingchuan Zhu, Shiying Dong, Yang Lin +4
Hardcore bosons on honeycomb lattice ribbons with zigzag edges are studied using exact numerical simulations. We map out the phase diagrams of ribbons with different widths, which…
Accurate Medical Named Entity Recognition Through Specialized NLP Models
Jiacheng Hu, Runyuan Bao, Yang Lin +2
This study evaluated the effect of BioBERT in medical text processing for the task of medical named entity recognition. Through comparative experiments with models such as BERT, Cl…
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
Xinyu Ma, Yifeng Xu, Yang Lin +5
We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning ar…
SBF: An Effective Representation to Augment Skeleton for Video-based Human Action Recognition
Zhuoxuan Peng, Yiyi Ding, Yang Lin +1
Many modern video-based human action recognition (HAR) approaches use 2D skeleton as the intermediate representation in their prediction pipelines. Despite overall encouraging resu…
Pt-based nanowire networks with enhanced oxygen-reduction activity
Henning Galinski, Thomas Ryll, Yang Lin +4
Pt-Al and Pt-Y-Al thin film electrodes on yttria-stabilised zirconia electrolytes were prepared by dealloying of co-sputtered Pt-Al or Pt-Y-Al films. The selective dissolution of A…
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms
Disha Ghandwani, Neeraj Sarna, Yuanyuan Li +1
Advanced classification algorithms are being increasingly used in safety-critical applications like health-care, engineering, etc. In such applications, miss-classifications made b…
Systematic validation of time-resolved diffuse optical simulators via non-contact SPAD-based measurements
Weijia Zhao, Linlin Li, Kaiqi Kuang +6
Objective: Time-domain diffuse optical imaging (DOI) requires accurate forward models for photon propagation in scattering media. However, existing simulators lack comprehensive ex…
MedDCR: Learning to Design Agentic Workflows for Medical Coding
Jiyang Zheng, Islam Nassar, Thanh Vu +5
Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unli…
Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning
Jincheng Hu, Yang Lin, Jihao Li +5
Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sourc…
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
Xin Gao, Yang Lin, Ruiqing Li +4
Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised too…
A Boundary Offset Prediction Network for Named Entity Recognition
Minghao Tang, Yongquan He, Yongxiu Xu +3
Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER t…
Some identities on Lin-Peng-Toh's partition statistic of -colored partitions
Yang Lin, Ernest X. W. Xia, Xuan Yu
Recently, Andrews proved two conjectures on a partition statistic introduced by Beck. Very recently, Chern established some results on weighted rank and crank moments and proved ma…
Enhancing LLM Medical Coding with Structured External Knowledge
Yidong Gan, David D. Nguyen, Yang Lin +4
Accurate medical coding requires consulting authoritative resources such as the ICD tabular list and coding guidelines. Existing LLM-based automated methods largely rely on LLMs' i…
Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology
Yang Lin, Muqing Li, Ziyi Zhu +3
The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a gr…
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting
Yang Lin
Multi-horizon time series forecasting, crucial across diverse domains, demands high accuracy and speed. While AutoRegressive (AR) models excel in short-term predictions, they suffe…
Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules
Yutian Yang, Hongjie Qiu, Yulu Gong +3
The 3D simulation model of the lung was established by using the reconstruction method. A computer aided pulmonary nodule detection model was constructed. The process iterates over…
Quantifying Correlations of Machine Learning Models
Yuanyuan Li, Neeraj Sarna, Yang Lin
Machine Learning models are being extensively used in safety critical applications where errors from these models could cause harm to the user. Such risks are amplified when multip…
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
Xinyu Ma, Xu Chu, Yasha Wang +4
Graph data augmentation has shown superiority in enhancing generalizability and robustness of GNNs in graph-level classifications. However, existing methods primarily focus on the…