Results 101 to 110 of about 714,450 (192)

Identifying Ordinal Similarities at Different Temporal Scales

Entropy
This study implements the permutation Jensen–Shannon distance as a metric for discerning ordinal patterns and similarities across multiple temporal scales in time series data.
Luciano Zunino, Xavier Porte, Miguel C. Soriano   +2 more
doaj   +1 more source

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

, 2021
Prior works have found it beneficial to combine provably noise-robust loss functions e.g. mean absolute error (MAE) with standard categorical loss function e.g. crossentropy (CE) to improve their learnability.
Englesson, Erik, Azizpour, Hossein
core   +1 more source

Directed Network Comparison Using Motifs

Entropy
Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between ...
Chenwei Xie, Qiao Ke, Haoyu Chen, Chuang Liu, Xiu-Xiu Zhan   +4 more
doaj   +1 more source

A Quantum Jensen-Shannon Graph Kernel for Unattributed Graphs [PDF]

, 2014
In this paper, we use the quantum Jensen-Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel.
Lu, Bai, Lu Bai, Hancock, Edwin R., Bai, Lu, Edwin R. Hancock, Edwin R., Hancock, Torsello, Andrea, Rossi, Luca   +7 more
core   +1 more source

Glial Dysfunction and Memory Impairments in a Model of Pediatric Obstructive Sleep Apnea

Glia, Volume 74, Issue 11, November 2026.
Generation and analysis of an snRNA‐seq atlas of the hippocampus in POSA. Validation of reduced protein expression of six genes across four cell types. Cellular‐level investigation of aberrant glial function in POSA using transgenic reporter mice. ABSTRACT Pediatric obstructive sleep apnea (POSA) is a common childhood disease that often causes aberrant
Michael R. Williamson, Jorge Botas, Mahyar J. Hedayatpour, Thea Zlatkov, Akdes Serin Harmanci, Christopher J. Yates, Kenneth Nobleza, Roshan Ailani, Adam C. Adler, Farrah Kheradmand, Hyun Kyoung Lee, Benjamin Deneen, Hyun‐Hwan Jeong, Arvind Chandrakantan   +13 more
wiley   +1 more source

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen-Shannon Divergence. [PDF]

Entropy (Basel), 2022
Levene M.
europepmc   +1 more source

Mission‐Aligned Learning‐Informed Control of Autonomous Systems: Formulation and Foundations

Expert Systems, Volume 43, Issue 11, November 2026.
ABSTRACT Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices.
Vyacheslav Kungurtsev, Alessandro Di Frenna, Gustav Šír, Monicah Cherop Naibei, Haozhe Tian, Homayoun Hamedmoghadam, Akhil Anand, Sebastien Gros   +7 more
wiley   +1 more source

An Active Sonar Signal Detection Algorithm Based on Weighted Neighbourhood Relative Entropy in the Underwater Acoustic Noise Environment

Journal of Marine Science and Engineering
This paper presents a target detection method for active sonar based on weighted neighbourhood relative entropy (WNRE), using tools from information geometry.
Ken Cheng, Jiaxi Cheng, Fangyong Wang, Xinyu Gu, Shuanping Du   +4 more
doaj   +1 more source

Epistemic Capabilities of Artificial Intelligence for Public Administration and Policy Research: A Framework for Scholarship in the Age of Intelligent Machines

Policy Studies Journal, Volume 54, Issue 4, November 2026.
ABSTRACT Artificial intelligence (AI) has gained much interest in public administration and public policy fields in recent years. Research on and that uses AI has so far treated it as an object or context within the broader administrative and governance discourse.
Yanto Chandra, Jianxiang Tan
wiley   +1 more source

Heatmap showing the Jensen–Shannon (JS) divergence between challenge groups estimated from FPKM values for all genes.

, 2015
Heatmap showing the Jensen–Shannon (JS) divergence between challenge groups estimated from FPKM values for all genes.
Polyana C. Tizioto (762238), Christopher M. Seabury (194409), JaeWoo Kim (544072), Laurel J. Gershwin (653896), Rachel Toaff-Rosenstein (762239), Holly L. Neibergs (127865), Alison L. Van Eenennaam (242082), Robert D. Schnabel (194392), Jeremy F. Taylor (179789)   +8 more
core   +1 more source