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

Biology is both an important application area and a source of motivation fordevelopment of advanced machine learning techniques. Although much attentionhas been paid to large and complex data sets resulting from high-throughputsequencing, advances in high-quality video recording technology have begun togenerate similarly rich data sets requiring sophisticated techniques from bothcomputer vision and time-series analysis. Moreover, just as studying geneexpression patterns in one organism can reveal general principles that apply toother organisms, the study of complex social interactions in an experimentallytractable model system, such as a laboratory ant colony, can provide generalprinciples about the dynamics of other social groups. Here, we focus on onesuch example from the study of reproductive regulation in small laboratorycolonies of more than 50 Harpegnathos ants. These ants can be artificiallyinduced to begin a ~20 day process of hierarchy reformation. Although theconclusion of this process is conspicuous to a human observer, it remainsunclear which behaviors during the transient period are contributing to theprocess. To address this issue, we explore the potential application ofOne-class Classification (OC) to the detection of abnormal states in antcolonies for which behavioral data is only available for the normal societalconditions during training. Specifically, we build upon the Deep Support VectorData Description (DSVDD) and introduce the Inner-Outlier Generator (IO-GEN)that synthesizes fake "inner outlier" observations during training that arenear the center of the DSVDD data description. We show that IO-GEN increasesthe reliability of the final OC classifier relative to other DSVDD baselines.This method can be used to screen video frames for which additional humanobservation is needed.

Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change