Data usually resides on a manifold, and the minimal dimension of such a manifold is called
its intrinsic dimension. This fundamental data property is not considered in the generative …

Complex data-parallel job contains task dependency information defined as Directed Acyclic
Graph (DAG). For convenience, the DAG presented data-parallel jobs are named as DAG …

Abstract During pandemics (eg, COVID-19) physicians have to focus on diagnosing and
treating patients, which often results in that only a limited amount of labeled CT images is …

Background Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease defined
using a number of well-established molecular subsets. Application of non-negative matrix …

In deep learning, the quality of ground truth training data is crucial for the resulting
performance. However, depending on applications, collecting a sufficient amount of quality …

We propose interest-driven progressive visual analytics. The core idea is to filter samples
with features of interest to analysts from the given dataset for analysis. The approach relies …

Somatic tumors have a high-dimensional, sparse, and small sample size nature, making
cancer subtype stratification based on somatic genomic data a challenge. Current methods …

Discovering and mitigating potential risks in advance is essential for preventing aviation
accidents on routine flights. Although anomaly detection-based explanation techniques …

Given the complexity of trauma presentations, particularly in those involving multiple areas
of the body, overlooked injuries are common during the initial assessment by a clinician. We …

Abstract Machine learning is a rapidly evolving field with vast potential. At its core, machine
learning research aims to develop broadly applicable algorithms capable of improving their …