Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays
Advanced Science, EarlyView.A reliability‐aware electronic nose is developed using photothermally anchored metal‐catalyst decorated metal oxide nanofiber sensor arrays combined with deep ensemble learning. Diverse catalytic nanofiber channels generate gas‐specific response patterns, enabling selective identification and quantification of sulfur‐containing gases.
Sungwoo Eo +5 more
wiley +1 more source
An Unobserved Components Model to Forecast Austrian GDP
This paper deals with forecasting quarterly Austrian GDP growth using monthly conjunctural indicators and state space models. The latter provide an efficient econometric framework to analyse jointly data with different frequencies.
Martin Spitzer, Gerhard Fenz
core
CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning
Advanced Science, EarlyView.CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang +11 more
wiley +1 more source
Total Factor Productivity: An Unobserved Components Approach [PDF]
This work examines the presence of unobserved components in the time series of Total Factor Productivity, which is an idea central to modern Macroeconomics. The main approaches in both the study of economic growth and the study of business cycles rely on
Raul Crespo
core
Are We in Control? How Best to Include a Control Group in Interrupted Time Series Designs: A Simulation Study. [PDF]
J Eval Clin PractManca F, Mackay D, Lewsey J.
europepmc +1 more source
Advanced Science, EarlyView.Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li +6 more
wiley +1 more source
Seasonality with Trend and Cycle Interactions in Unobserved Components Models
Unobserved components time series models decompose a time series into a trend, a season, a cycle, an irregular disturbance, and possibly other components. These models have been successfully applied to many economic time series.
Kai Ming Lee, Siem Jan Koopman
core
Empirical analysis of business cycles using the unobserved components model
, 2018 Thesis by publication.Includes bibliographical references.1. Introduction -- 2. Okun coefficients and participation coefficients -- 3. Average labour productivity dynamics over the business cycle -- 4.
Evans, Andrew E
core
Resolving non-identifiability mitigates systematic errors in simultaneous models of neural tuning and functional coupling. [PDF]
J Comput NeurosciSachdeva P +6 more
europepmc +1 more source
Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates
Advanced Science, EarlyView.Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao +4 more
wiley +1 more source