Search Results for author:
Found 51 papers, 18 papers with code
On the Hecke algebras and the colored HOMFLY polynomial
The colored HOMFLY polynomial is the quantum invariant of oriented links in $S^3$ associated with irreducible representations of the quantum group $U_q(\mathrm{sl}_N)$.
Quantum Algebra Geometric Topology
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
An open-source Mandarin speech corpus called AISHELL-1 is released.
MLE-induced Likelihood for Markov Random Fields
Especially as the size of the MRF increases, both the numerical performance and the computational cost of our approach remain consistently satisfactory, whereas Laplace approximation deteriorates and pseudolikelihood becomes computationally unbearable.
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation.
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation
Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively.
Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks
To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
Ranked #1 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.
BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT model
In this study we deal with the problem of identifying and categorizing offensive language in social media.
Sentence Centrality Revisited for Unsupervised Summarization
Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets.
AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks
Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation.
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging.
Mining Message Flows using Recurrent Neural Networks for System-on-Chip Designs
Initial results show that the proposed approach is capable of extracting various patterns on system-level specifications from the highly concurrent SoC execution traces.
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Diagnosis of electron density and temperature by using collisional radiative model in capacitively coupled Ar plasmas I: triple-frequency discharges
Compared with the dual-frequency (DF) CCPs, it is found that with the increase of IF power, the HF power can control the electron density more independently with less influence on the electron temperature.
Plasma Physics
Diagnosis of electron density and temperature by using collisional radiative model in capacitively coupled Ar plasmas II: two-dimensional distributions
Effects of radio-frequency power and driven frequency on the two-dimensional (axial and radial) distributions of electron density and temperature were experimentally investigated in low pressure capacitively coupled argon plasmas.
Plasma Physics
Compositional Generalization via Semantic Tagging
Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i. e., they are unable to systematically generalize to unseen compositions of seen components.
Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation
Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways.
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
Categorical computation
We provide a theoretical idea of realizing (higher) functors physically based on the physics of topological orders.
Quantum Physics Strongly Correlated Electrons Category Theory
Model Synthesis for Communication Traces of System-on-Chip Designs
In this paper, we consider the problem of inferring models from communication traces of system-on-chip~(SoC) designs.
LenAtten: An Effective Length Controlling Unit For Text Summarization
Fixed length summarization aims at generating summaries with a preset number of words or characters.
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation
Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.
Disentangled Sequence to Sequence Learning for Compositional Generalization
There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components.
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches.
LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation
Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans.
Deep Bidirectional Transformers for SoC Flow Specification Mining
High-quality system-level message flow specifications can lead to comprehensive validation of system-on-chip (SoC) designs.
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.
Mining SoC Message Flows with Attention Model
High-quality system-level message flow specifications are necessary for comprehensive validation of system-on-chip (SoC) designs.
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention
In this paper, we propose a brain-inspired hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs).
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard.
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning
Compositional generalization is a basic mechanism in human language learning, which current neural networks struggle with.
Deep Clustering Survival Machines with Interpretable Expert Distributions
We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions.
SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces, SurfNN reconstructs both the inner and outer cortical surfaces jointly by training a single network to predict a midthickness surface that lies at the center of the inner and outer cortical surfaces.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Caption Anything: Interactive Image Description with Diverse Multimodal Controls
Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, $\textit{e. g.}$, looking at the specified regions or telling in a particular text style.
CageViT: Convolutional Activation Guided Efficient Vision Transformer
Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input sequence.
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult.
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations.
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset.
Can Machine Learning Uncover Insights into Vehicle Travel Demand from Our Built Environment?
In this paper, we propose a machine learning-based approach to address the lack of ability for designers to optimize urban land use planning from the perspective of vehicle travel demand.
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.
Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification
In current synthetic aperture radar (SAR) object classification, one of the major challenges is the severe overfitting issue due to the limited dataset (few-shot) and noisy data.
Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification
Most existing synthetic aperture radar (SAR) ship classification technologies heavily rely on correctly labeled data, ignoring the discriminative features of unlabeled SAR ship images.
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization
The single domain generalization(SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem.
A Multi-Expert Large Language Model Architecture for Verilog Code Generation
Recently, there has been a surging interest in using large language models (LLMs) for Verilog code generation.
