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In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements.
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This article treats each signal as independent of its spatial or temporal neighbors.
Our k-nearest temporal neighbors algorithm finds temporal profiles that are likely to be similar to the venue of interest.
In this work, we propose an unsupervised anomaly detection framework that requires no prior knowledge and is capable of detecting anomalous events, which we define as groups of outlier objects differing contextually from their spatial and temporal neighbors.
An active condition was assigned a '+' if its FA-value was higher than the average of the FA-values of its two temporal neighbors (the rest conditions just before and just after the rest conditions), otherwise a '−' was assigned.
This randomization ensured that each stimulus was at least 4 JNDs from its temporal neighbors, and normally more than 4 JNDs.
We first look at the value of NRMSE as we vary the number of neighbors N. Our results show (N = 10) to be the best indicator of temporal similarity of neighbors.
We have added text to the Discussion to make this point and to present clearly all the directions in which the modeling could be extended: neighbor-neighbor interactions, temporal nonlinearities, post-spike filter, RF surround, and inhibitory inputs.
As most nearest-neighbors are coincidental, little difference was seen in the mean temporal and spatial distance between nearest-neighbors of the same serosubtype (6.1 km [range 0 44 km] and 13.2 days [range 0 63 days]) and those of different serosubtype (7.6 km [range 0 49 km] and 14.3 days [range 0 380 days]).
(2) Conduct label transfer in an efficient nearest neighbor search and a temporal MRF model.
First, its direct neighbors also have better temporal betweenness than static betweenness.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com