o improve accuracy, Sony hasintroduced the Weighted Least Square Method (WLSM) al-gorithm. In WLSM, the square value of residuals are individu-ally weighted

Avoid the use of spectral schemes to represent sequential data because the spectral order of visible light carries no inherent magnitude message. Readers do not automatically perceive violet as greater than red even though the two colors occupy opposite ends of the color spectrum. Rainbow color schemes are therefore not appropriate if the data to be mapped or graphed represent a distribution of values ranging from low to high

Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

On the Spectral Bias of Deep Neural Networks

Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning

rid. The apparent equivalency of thesetime chunksmask the affective experiences and emotional intensities of lived temporality

. Notably, this lack of predictability had large social, rather than individual, implications—a finding that strongly echoes prior CSCW research [1, 4, 13, 15, 20, 22, 34, 35, 36, 38, 45, 46, 59]

In alignmentwith Reddy and Dourish’s concept of temporal trajectory [45], spectral time suggests that temporal experience is more than a grid of accountable blocks; multiple temporalities create flowsthat often defy both logical renderingand seamless manipulation

Our data reveal that not every temporal experience is easily articulated, planned for, measurable orable to be renderedinto a schedule.We call these temporal experiences spectral time,to capture howtime trailsor ghostsin ways that cannot always be expected, planned, or accounted for.Spectral time referencesmoments that do not lend themselves to scheduling(i.e. chunking), either because the act seems toomundane to justify articulation (i.e., getting dressed), because it is difficult to assess (i.e., travel time) or simply because it cannot be anticipated (i.e., creative phases).