No abstract

This paper presented the releasing behaviors of image sticking. Diffusion effect plays the critical role to release image sticking; therefore, higher temperature accelerates the releasing behavior. The releasing speed at temperature of 60C is faster than that at 25C by a factor of 6 in our demonstration.

This article introduces the solutions to improve LCD HDR performance. Backlight solutions including passive, active driving mini LED backlight and halo effects are studied. High cell transmittance and enhanced TFT device reliability for high brightness are discussed. Furthermore, the vivid image and high contrast can be kept in high ambient light by applying AUO A.R.T. technology.

New super‐multi‐domain polymer sustained alignment (PSA) liquid crystal displays are developed With this new pixel architecture, premium picture quality with very little oblique gamma distortion and color shift (Δ u‘v’ < 0.02) like in‐plane switching (IPS) mode, is obtained. Moreover, it can also realize low‐crosstalk patterned retarder 3D displays without sacrificing the 2D‐mode luminance.

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of heterogenous multi-phase data with possibly new clinical scenarios and pathologies. To do this, we propose a versatile framework that fuses appearance-based semi-supervision, maskbased adversarial domain adaptation, and pseudo-labeling. We also introduce coheterogeneous training, which is a novel integration of co-training and heteromodality learning. We have evaluated CHASe using a clinically comprehensive and challenging dataset of multi-phase computed tomography (CT) imaging studies (1147 patients and 4577 3D volumes). Compared to previous state-of-the-art baselines, CHASe can further improve pathological liver mask Dice-Sørensen coefficients by ranges of 4.2% ∼ 9.4%, depending on the phase combinations: e.g., from 84.6% to 94.0% on non-contrast CTs.