Framing emission gain layers for perovskite light-emitting diodes using polycaprolactone-silver nanoparticles featuring Förster resonance energy transfer and Purcell effects.

In this study, a new emission gain layer for perovskite light-emitting diodes (PeLEDs) is presented to improve their performance. The emission gain layer consisting of absorption-stable silver nanoparticles is prepared using the post-addition method of the polycaprolactone capping agent (PCL@AgNPs-P). This layer (PCL@AgNPs-P) effectively improves the Förster resonance energy transfer (FRET) between the low- (minor) and high- (major) phases in a quasi2D perovskite system, thereby increasing the major emission intensity and efficiency.

HuBar: A Visual Analytics Tool to Explore Human Behavior based on fNIRS in AR Guidance Systems.

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback.

Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User Feedback.

Visual data stories can effectively convey insights from data, yet their creation often necessitates intricate data exploration, insight discovery, narrative organization, and customization to meet the communication objectives of the storyteller. Existing automated data storytelling techniques, however, tend to overlook the importance of user customization during the data story authoring process, limiting the system's ability to create tailored narratives that reflect the user's intentions. We present a novel data story generation workflow that leverages adaptive machine-guided elicitation of user feedback to customize the story.

ARGUS: Visualization of AI-Assisted Task Guidance in AR.

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight.

Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-Link Diagrams.

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives.