Search Results for author:
Found 49 papers, 17 papers with code
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering
Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking
Moreover, we supplement the annotation of supporting span for MultiWOZ 2. 1, which is the shortest span in utterances to support the labeled value.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power.
How Curvature Enhance the Adaptation Power of Framelet GCNs
Motivated by the geometric analogy of Ricci curvature in the graph setting, we prove that by inserting the curvature information with different carefully designed transformation function $\zeta$, several known computational issues in GNN such as over-smoothing can be alleviated in our proposed model.
Frameless Graph Knowledge Distillation
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy.
Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision
We improve the reconstruction quality of complex geometry scene regions with sparse depth obtained by using the geometric constraints.
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion
We conduct a convergence analysis on pL-UFG, addressing the gap in the understanding of its asymptotic behaviors.
A Study of Generative Large Language Model for Medical Research and Healthcare
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
Improved Inner Approximation for Aggregating Power Flexibility in Active Distribution Networks and its Applications
Concise and reliable modeling for aggregating power flexibility of distributed energy resources in active distribution networks (ADNs) is a crucial technique for coordinating transmission and distribution networks.
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.
Cloud removal Using Atmosphere Model
Cloud removal is an essential task in remote sensing data analysis.
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
Tractable Data Enriched Distributionally Robust Chance-Constrained CVR
To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method.
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
When training is finished, some gates are exact zero, while others are around one, which is particularly favored by the practical hot-start training in the industry, due to no damage to the model performance before and after removing the features corresponding to exact-zero gates.
Online Feedback Droop Scheduling in Distribution Grids for Frequency and Local Voltage Control
This paper presents a novel framework for collective control of Distributed Energy Resources (DERs) in active Distribution Networks (DNs).
Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networks
The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators.
Global-Aware Registration of Less-Overlap RGB-D Scans
We propose a novel method of registering less-overlap RGB-D scans.
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning
Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals.
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization
During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin.
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models
In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes).
Clinical Relation Extraction Using Transformer-based Models
The goal of this study is to systematically explore three widely used transformer-based models (i. e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain.
A Discussion On the Validity of Manifold Learning
In this work, we investigate the validity of learning results of some widely used DR and ManL methods through the chart mapping function of a manifold.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Casting voids in nickel superalloy and the mechanical behaviour under room temperature tensile deformation
The volume fraction of the voids increases to 0. 21 (+-0. 001) vol% after tensile deformation.
Materials Science
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification
This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
Depth Edge Guided CNNs for Sparse Depth Upsampling
Inspired by the normalized convolution operation, we propose a guided convolutional layer to recover dense depth from sparse and irregular depth image with an depth edge image as guidance.
Emotion Recognition From Gait Analyses: Current Research and Future Directions
This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns.
CosmoVAE: Variational Autoencoder for CMB Image Inpainting
The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
Sparse optimal control of networks with multiplicative noise via policy gradient
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks.
Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
Among American women, the rate of breast cancer is only second to lung cancer.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN
PURPOSE: To apply deep CNN to the segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging and to develop methods that measure uncertainty and that adapt the CNN model to a specific false positive vs. false negative tradeoff.
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part II: Case studies
Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance.
Optimization and Control Systems and Control
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part I: Methodologies
We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
Tractable Clustering of Data on the Curve Manifold
However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.
Collaborative Low-Rank Subspace Clustering
In this paper we present Collaborative Low-Rank Subspace Clustering.
Efficient Sparse Subspace Clustering by Nearest Neighbour Filtering
Sparse Subspace Clustering (SSC) has been used extensively for subspace identification tasks due to its theoretical guarantees and relative ease of implementation.
Low-rank Multi-view Clustering in Third-Order Tensor Space
Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.
Low-Rank Representation over the Manifold of Curves
However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.
l1-norm Penalized Orthogonal Forward Regression
A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).
Segmentation of Subspaces in Sequential Data
We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces.
Subspace Clustering for Sequential Data
We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces.
