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Abstract We have studied effects of metal–dielectric substrates on photopolymerization of [2,2ʹ-Bi-1H-indene]-1,1ʹ-dione-3,3ʹ-diyl diheptanoate (BITh) monomer. We synthetized BITh and spin-coated it onto a variety of dielectric, metallic, and metal–dielectric substrates. The films were exposed to radiation of a UV–Visible Xe lamp, causing photo-polymerization of monomer molecules. The magnitude and the rate of the photo-polymerization were monitored by measuring the strength of the ~ 480 nm absorption band, which existed in the monomer but not in the polymer. Expectedly, the rate of photo-polymerization changed nearly linearly with the change of the pumping intensity. In contrast with our early study of photo-degradation of semiconducting polymer P3HT, the rate of photo-polymerization of BITh is getting modestly higher if the monomer film is deposited on top of silver separated from the monomer by a thin insulating MgF 2 layer preventing a charge transfer. This effect is partly due to a constructive interference of the incident and reflected light waves, as well as known in the literature effects of metal/dielectric substrates on a variety of spectroscopic and energy transfer parameters. At the same time, the rate of photopolymerization is getting threefold larger if monomer is deposited on Ag film directly and charge transfer is allowed. Finally, Au substratesmore »cause modest (~ 50%) enhancement of both monomer film absorption and the rate of photo-polymerization.« less

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized the promised potential due to a lack of insight into pathology and HCI considerations for pathologists’ navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists’ domain knowledge, we designed NaviPath — a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that participants’ navigation was more consistent with NaviPath, which can improve the examination quality.

The core of self-supervised learning for pre-training language models includes pre-training task design as well as appropriate data augmentation. Most data augmentations in language model pre-training are context-independent. A seminal contextualized augmentation was recently proposed in ELECTRA and achieved state-of-the-art performance by introducing an auxiliary generation network (generator) to produce contextualized data augmentation for the training of a main discrimination network (discriminator). This design, however, introduces extra computation cost of the genera- tor and a need to adjust the relative capability between the generator and the discriminator. In this paper, we propose a self-augmentation strategy (SAS) where a single network is utilized for both regular pre-training and contextualized data augmentation for the training in later epochs. Essentially, this strategy eliminates a separate generator and uses the single network to jointly conduct two pre-training tasks with MLM (Masked Language Modeling) and RTD (Replaced Token Detection) heads. It avoids the challenge to search for an appropriate size of the generator, which is critical to the performance as evidenced in ELECTRA and its subsequent variant models. In addition, SAS is a general strategy that can be seamlessly combined with many new techniques emerging recently or in the future, such as the disentangled attentionmore »mechanism from DeBERTa. Our experiments show that SAS outperforms ELECTRA and other state-of-the-art models in the GLUE tasks with similar or less computation cost.« less

Large meridional excursions of a jet stream are conducive to blocking and related midlatitude weather extremes, yet the physical mechanism of jet meandering is not well understood. This paper examines the mechanisms of jet meandering in boreal winter through the lens of a potential vorticity (PV)-like tracer advected by reanalysis winds in an advection–diffusion model. As the geometric structure of the tracer displays a compact relationship with PV in observations and permits a linear mapping from tracer to PV at each latitude, jet meandering can be understood by the geometric structure of tracer field that is only a function of prescribed advecting velocities. This one-way dependence of tracer field on advecting velocities provides a new modeling framework to quantify the effects of time mean flow versus transient eddies on the spatiotemporal variability of jet meandering. It is shown that the mapped tracer wave activity resembles the observed spatial pattern and magnitude of PV wave activity for the winter climatology, interannual variability, and blocking-like wave events. The anomalous increase in tracer wave activity for the composite over interannual variability or blocking-like wave events is attributed to weakened composite mean winds, indicating that the low-frequency winds are the leading factor formore »the overall distributions of wave activity. It is also found that the tracer model underestimates extreme wave activity, likely due to the lack of feedback mechanisms. The implications for the mechanisms of jet meandering in a changing climate are also discussed.

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