Model-agnostic meta-learning-based region-adaptive parameter adjustment scheme for influenza forecasting

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to alleviate this problem by embedding common prior knowledge from different tasks into the initial parameters of the target model. Data shortages are very common in regional influenza predictions, and MAML also often struggles with regional influenza forecasting, especially when region-specific knowledge, such as peak timing or intensity, varies. In this paper, we propose a novel MAML-based parameter adjustment scheme for influenza forecasting, called MARAPAS. The fundamental idea of our scheme is to adjust the initial parameters obtained from common knowledge to a target region by using adjustment variables. We experimentally show that MARAPAS outperforms other MAML-based methods, in terms of root mean square error and Pearson correlation coefficient. Particularly, this scheme improves the forecasting performance by up to 34 % compared with that of the state-of-the-art schemes. We also show the robust forecasting accuracy of our scheme and demonstrate its applicability by performing zero-shot COVID-19 forecasting

Facile Ligand Exchange of Ionic Ligand-Capped Amphiphilic Ag₂S Nanocrystals for High Conductive Thin Films

A surface ligand modification of colloidal nanocrystals (NCs) is one of the crucial issues for their practical applications because of the highly insulating nature of native long-chain ligands. Herein, we present straightforward methods for phase transfer and ligand exchange of amphiphilic Ag2S NCs and the fabrication of highly conductive films. S-terminated Ag2S (S–Ag2S) NCs are capped with ionic octylammonium (OctAH+) ligands to compensate for surface anionic charge, S2–, of the NC core. An injection of polar solvent, formamide (FA), into S–Ag2S NCs dispersed in toluene leads to an additional envelopment of the charged S–Ag2S NC core by FA due to electrostatic stabilization, which allows its amphiphilic nature and results in a rapid and effective phase transfer without any ligand addition. Because the solvation by FA involves a dissociation equilibrium of the ionic OctAH+ ligands, controlling a concentration of OctAH+ enables this phase transfer to show reversibility. This underlying chemistry allows S–Ag2S NCs in FA to exhibit a complete ligand exchange to Na+ ligands. The S–Ag2S NCs with Na+ ligands show a close interparticle distance and compatibility for uniformly deposited thin films by a simple spin-coating method. In photoelectrochemical measurements with stacked Ag2S NCs on ITO electrodes, a 3-fold enhanced current response was observed for the ligand passivation of Na+ compared to OctAH+, indicating a significantly enhanced charge transport in the Ag2S NC film by a drastically reduced interparticle distance due to the Na+ ligands

Plasma information-based virtual metrology (PI-VM) and mass production process control

© 2022, The Korean Physical Society.In this paper, we review the development of plasma engineering technology that improves dramatically the production efficiency of OLED (organic light-emitting diode) displays and semiconductor manufacturing by utilizing a process monitoring methodology based on the physical domain knowledge. The domain knowledge consists of plasma-heating and sheath physics, plasma chemistry and plasma-material surface reaction kinetics, and plasma diagnostics. Based on this, a plasma information-based virtual metrology (PI-VM) algorithm was developed drastically enhanced process prediction performance by parameterizing plasma information (PI) which can trace the states of processing plasmas. PI-VM has superior process prediction accuracy compared to the classical statistics-based virtual metrologies. The developed PI-VM algorithms adopted for practical processing issues such as the control and management of the OLED-display mass production demonstrated savings of approximately 25% of the yield loss over the past 5 years. This improvement was achieved with the development of FDC (fault detection and classification) and APC (advanced process control) logic, which can be developed through the analysis of the physical characteristics of the feature parameters used in PI-VM with the evaluation of their contributions and their correlations to the processing results. PI-VM provides leverage that can be applied in the development of process equipment and factory automation technologies.N