The simulation of supersonic or hypersonic flows often suffers from nume...
The shock instability problem commonly arises in flow simulations involv...
Datasets with sheer volume have been generated from fields including com...
Modern shock-capturing schemes often suffer from numerical shock anomali...
Spike sorting, which classifies spiking events of different neurons from...
Root Cause Analysis (RCA) plays an indispensable role in distributed dat...
Robotic grasping aims to detect graspable points and their corresponding...
Obstacle avoidance is an essential topic in the field of autonomous dron...
Despite achieving state-of-the-art zero-shot performance, existing
visio...
Knowledge Base Question Answering (KBQA) aims to answer userquestions fr...
Due to the success of Bidirectional Encoder Representations from Transfo...
Accurate animal pose estimation is an essential step towards understandi...
We study numerical conformal mappings of planar Jordan domains with
boun...
This work presents a probabilistic channel pruning method to accelerate
...
Many methods have been proposed to detect concept drift, i.e., the chang...
Nonparametric feature selection in high-dimensional data is an important...
In this paper, a unified framework to develop all-speed HLL-type schemes...
Feature selection is important in data representation and intelligent
di...
Parametric linear programming is a central operation for polyhedral
comp...
Estimating time-varying graphical models are of paramount importance in
...
Adversarial examples are inputs with imperceptible perturbations that ea...
Polyhedral projection is a main operation of the polyhedron abstract
dom...
Deep neural networks have been found vulnerable to noises like adversari...
Deep neural networks (DNNs) are vulnerable to adversarial examples where...
Adversarial examples, intentionally designed inputs tending to mislead d...
Parametric linear programming is central in polyhedral computations and ...