Stimulant use disorder and the likelihood of stroke: Analysis of a national database in the United States.

Background: Stimulant use has been associated with an increased risk of stroke, though data on clinical characteristics and exact risk are limited. This retrospective case-control study examines stroke risk in individuals with stimulant use disorder using data from a national U.S.

"UDE DIATOMS in the Wild 2024": a new image dataset of freshwater diatoms for training deep learning models.

Background: Diatoms are microalgae with finely ornamented microscopic silica shells. Their taxonomic identification by light microscopy is routinely used as part of community ecological research as well as ecological status assessment of aquatic ecosystems, and a need for digitalization of these methods has long been recognized. Alongside their high taxonomic and morphological diversity, several other factors make diatoms highly challenging for deep learning-based identification using light microscopy images.

Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos.

High Dynamic Range (HDR) videos are able to represent wider ranges of contrasts and colors than Standard Dynamic Range (SDR) videos, giving more vivid experiences. Due to this, HDR videos are expected to grow into the dominant video modality of the future. However, HDR videos are incompatible with existing SDR displays, which form the majority of affordable consumer displays on the market.

One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment.

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines.

A Study of Subjective and Objective Quality Assessment of HDR Videos.

As compared to standard dynamic range (SDR) videos, high dynamic range (HDR) content is able to represent and display much wider and more accurate ranges of brightness and color, leading to more engaging and enjoyable visual experiences. HDR also implies increases in data volume, further challenging existing limits on bandwidth consumption and on the quality of delivered content. Perceptual quality models are used to monitor and control the compression of streamed SDR content.