English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We first introduce a novel profile-based alignment algorithm, the multiplecontinuous Signal Alignment algorithm with Gaussian Process Regression profiles(SA-GPR). SA-GPR addresses the limitations of currently available signalalignment methods by adopting a hybrid of the particle smoothing andMarkov-chain Monte Carlo (MCMC) algorithms to align signals, and by applyingthe Gaussian process regression to construct profiles to be alignedcontinuously. SA-GPR shares all the strengths of the existing alignmentalgorithms that depend on profiles but is more exact in the sense that profilesdo not need to be discretized as sequential bins. The uncertainty ofperformance over the resolution of such bins is thereby eliminated. Thismethodology produces alignments that are consistent, that regularize extremecases, and that properly reflect the inherent uncertainty.  Then we extend SA-GPR to a specific problem in the field of paleoceanographywith a method called Bayesian Inference Gaussian Process Multiproxy Alignmentof Continuous Signals (BIGMACS). The goal of BIGMACS is to infer continuousages for ocean sediment cores using two classes of age proxies: proxies thatexplicitly return calendar ages (e.g., radiocarbon) and those used tosynchronize ages in multiple marine records (e.g., an oxygen isotope basedmarine proxy known as benthic ${\delta}^{18}{\rm O}$). BIGMACS integrates thesetwo proxies by iteratively performing two steps: profile construction frombenthic ${\delta}^{18}{\rm O}$ age models and alignment of each core to theprofile also reflecting radiocarbon dates. We use BIGMACS to construct a newDeep Northeastern Atlantic stack (i.e., a profile from a particular benthic${\delta}^{18}{\rm O}$ records) of five ocean sediment cores. We conclude byconstructing multiproxy age models for two additional cores from the sameregion by aligning them to the stack.

Bayesian Inference Gaussian Process Multiproxy Alignment of Continuous Signals (BIGMACS): Applications for Paleoceanography