Comparison of two digital intraoral radiography imaging systems as a function of contrast resolution and exposure time

BACKGROUND To compare the image quality of two different digital imaging systems; one photostimulable phosphor plate system (PSP) and a direct digital radiography system with CMOS imaging sensor; via evaluating contrast resolution among four different exposure times. METHODS Endodontically treated incisor teeth embedded in paraffin blocks are aligned next to a 99.5% Al wedge and exposed for 0.8, 0.1,0.125 and 0.16 seconds using both the CMOS and PSP systems. Using ImageJ software, 5 isometric and isogridded ROI from each root filling area and isometric ROI from the Al stepwedge were calculated. RESULTS Evaluation of the total of 120 images displayed that PSP system produced significantly higher contrast resolution (P0.05). CONCLUSIONS The contrast resolution was higher using the PSP system. It can be estimated that, filling material will be more obvious under lower doses using PSP

Pressure-tuning of the c-f hybridization in Yb metal detected by infrared spectroscopy up to 18 GPa

It has been known that the elemental Yb, a divalent metal at mbient pressure, becomes a mixed-valent metal under external pressure, with its valence reaching ~2.6 at 30 GPa. In this work, infrared spectroscopy has been used to probe the evolution of microscopic electronic states associated with the valence crossover in Yb at external pressures up to 18 GPa. The measured infrared reflectivity spectrum R(w) of Yb has shown large variations with pressure. In particular, R(w) develops a deep minimum in the mid-infrared, which shifts to lower energy with increasing pressure. The dip is attributed to optical absorption due to a conduction c-f electron hybridization state, similarly to those previously observed for heavy fermion compounds. The red shift of the dip indicates that the $c$-$f$ hybridization decreases with pressure, which is consistent with the increase of valence.Comment: 2 pages, to appear in J. Phys. Soc. Jpn. Supp

Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach

Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data. However, off-policy learning becomes challenging when the discrepancy between the underlying distributions of the agent's policy and collected data increases. Although the well-studied importance sampling and off-policy policy gradient techniques were proposed to compensate for this discrepancy, they usually require a collection of long trajectories and induce additional problems such as vanishing/exploding gradients or discarding many useful experiences, which eventually increases the computational complexity. Moreover, their generalization to either continuous action domains or policies approximated by deterministic deep neural networks is strictly limited. To overcome these limitations, we introduce a novel policy similarity measure to mitigate the effects of such discrepancy in continuous control. Our method offers an adequate single-step off-policy correction that is applicable to deterministic policy networks. Theoretical and empirical studies demonstrate that it can achieve a "safe" off-policy learning and substantially improve the state-of-the-art by attaining higher returns in fewer steps than the competing methods through an effective schedule of the learning rate in Q-learning and policy optimization

How Universal Is the Relationship Between Remotely Sensed Vegetation Indices (VI) and Crop Leaf Area Index (LAI)?

Global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. This research enables the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research