Results 71 to 80 of about 21,280,601 (295)

Knowledge graph construction with structure and parameter learning for indoor scene design

Computational Visual Media, 2018
We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor
Yuan Liang, Fei Xu, Song-Hai Zhang, Yu-Kun Lai, Taijiang Mu   +4 more
doaj   +1 more source

Structure propagation for zero-shot learning

, 2017
The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes).
CH Lampert, E Zhang, G Lin, G Lin, G Lin, G Patterson, GJ Qi, Y Fu, Y Zhang, Z Akata   +9 more
core   +1 more source

Prolonged Corrected QT Interval as an Early Electrocardiographic Marker of Cyclophosphamide‐Induced Cardiotoxicity in Pediatric Hematology and Oncology Patients

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Cyclophosphamide (CY) is associated with potentially fatal cardiotoxicity, yet no electrocardiographic indices have been established for early detection of CY‐induced cardiomyopathy. This study aimed to determine whether corrected QT interval (QTc) prolongation can predict early onset of CY‐related cardiac dysfunction in pediatric ...
Junpei Kawamura, Kentaro Ueno, Takuro Nishikawa, Yoshihiro Takahashi, Koji Nakae, Yasuhiro Okamoto   +5 more
wiley   +1 more source

An adaptive feature selection algorithm based on MDS with uncorrelated constraints for tumor gene data classification

Mathematical Biosciences and Engineering, 2023
The developing of DNA microarray technology has made it possible to study the cancer in view of the genes. Since the correlation between the genes is unconsidered, current unsupervised feature selection models may select lots of the redundant genes ...
Wenkui Zheng, Guangyao Zhang, Chunling Fu , Bo Jin   +3 more
doaj   +1 more source

Structure learning of antiferromagnetic Ising models [PDF]

, 2014
In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i.i.d. samples. We first observe that the notoriously difficult problem of learning parities with noise can be
Bresler, Guy, Gamarnik, David, Shah, Devavrat   +2 more
core   +1 more source

Psychological Safety Among Interprofessional Pediatric Oncology Teams in Germany: A Nationwide Survey

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Psychological safety (PS) is essential for teamwork, communication, and patient safety in complex healthcare environments. In pediatric oncology, interprofessional collaboration occurs under high emotional and organizational demands. Low PS may increase stress, burnout, and adverse events.
Alexandros Rahn, Anna‐Lena Herbach, Christian Bock, Katharina Fleig, Urs Mücke   +4 more
wiley   +1 more source

Hidden Variable Discovery Based on Regression and Entropy

Mathematics
Inferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some ...
Xingyu Liao, Xiaoping Liu
doaj   +1 more source

Information Theoretic Structure Learning with Confidence

, 2016
Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension.
Hero III, Alfred O., Moon, Kevin R., Noshad, Morteza, Sekeh, Salimeh Yasaei   +3 more
core   +1 more source

Spatio-Temporal Graph Structure Learning for Traffic Forecasting

AAAI Conference on Artificial Intelligence, 2020
As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects.
Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan   +4 more
semanticscholar   +1 more source

Structural learning

Scholarpedia, 2013
Structural learning in motor control refers to a metalearning process whereby an agent extracts (abstract) invariants from its sensorimotor stream when experiencing a range of environments that share similar structure. Such invariants can then be exploited for faster generalization and learning-to-learn when experiencing novel, but related task ...
openaire   +3 more sources