Results 101 to 110 of about 6,826 (184)
A quantum Jensen-Shannon graph kernel using discrete-time quantum walks [PDF]
, 2015 In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a density matrix over each graph being compared.
Ren, Peng +9 more
core +1 more source
Applied Economic Perspectives and Policy, Volume 48, Issue 3, Page 545-564, July 2026.ABSTRACT We link American Community Survey and SNAP records for 185,000 units with ground‐sourced social food infrastructure data from FindFoodIL (Illinois Extension SNAP‐Ed) to examine SNAP participation determinants among eligible units. Bivariate probit models reveal, beyond SNAP offices, quantity of social infrastructure is associated with ...
Michael Lotspeich‐Yadao +3 more
wiley +1 more source
Entropy Analysis of Soccer Dynamics
Entropy, 2019 This paper adopts the information and fractional calculus tools for studying the dynamics of a national soccer league. A soccer league season is treated as a complex system (CS) with a state observable at discrete time instants, that is, at the time of ...
António M. Lopes +1 more
doaj +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
Magnetic Resonance in Medicine, Volume 96, Issue 1, Page 238-246, July 2026.ABSTRACT Purpose To develop and evaluate a motion‐compensated diffusion imaging with phase‐contrast (MC‐DIP) technique for mitigating errors in regional cerebral blood flow (rCBF) quantification caused by physiological brain motion. Methods Diffusion‐weighted images were acquired in 11 healthy volunteers on a 3.0 T MRI system using three gradient ...
Naoki Ohno +6 more
wiley +1 more source
A new method to measure the divergence in evidential sensor data fusion
International Journal of Distributed Sensor Networks, 2019 Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other.
Yutong Song, Yong Deng
doaj +1 more source
Water Resources Research, Volume 62, Issue 7, July 2026.Abstract Offsets and distributional discrepancies between runoff simulations and observations were two important error sources which remarkably degraded runoff prediction performance of hydrological models. However, traditional single post‐processing correction methods were difficult to reduce these two error sources simultaneously due to their ...
Xiaochuan Luo +7 more
wiley +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
Diverse Microbial Communities Assemble on Both Recalcitrant and Labile Carbon Sources
Environmental Microbiology, Volume 28, Issue 7, July 2026.Taxonomically and functionally distinct communities assemble on cellulose and its monomer, glucose. Although single carbon sources were provided, high and comparable diversity was maintained in both environments, supported by extensive cross‐feeding and scavenging. However, glucose supported higher biomass than cellulose and selected generalists, while
Kaumudi H. Prabhakara +4 more
wiley +1 more source
The Representation Jensen-Shannon Divergence
Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due to unknown underlying distributions.
Hoyos-Osorio, Jhoan K. +1 more
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