Latest AI and machine learning research in work force for healthcare professionals.
Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a single visual interpretation. Existing methods to improve diversity produce outputs driven by incidental variations rather than meaningful design choices. This motivates a new variant of the diversity task where structur...
Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization has become a dominant strategy for token reduction, existing methods rely on cosine-based normalized similarity that discards magnitude information, failing to faithfully approximate the original feature representation a...
Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matching ...
Machine-learning survival models are increasingly proposed for intensive-care mortality prediction and are almost always selected and reported using t...
As a cornerstone of the central dogma, RNA has both witnessed and actively shaped three billion years of evolution. Over this vast timescale, a remark...
The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distrib...
Abstract Introduction In-hospital cardiac arrest carries high mortality despite standardized ACLS training. Educators face increasing time constraints...
Antibodies are powerful therapeutics whose antigen specificity arises from sequence diversity shaped during development. Recently, language models tra...
Down syndrome, caused by trisomy 21, increases the risk of diverse co-occurring conditions. With more than 34,000 related publications indexed in PubM...
Background: Digital decision-support tools such as triage systems and symptom checkers support millions of health-related decisions each year. Their q...
A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-income countries, where over half of pregnant women ...
Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives deliver strong text-image alignment and high visual q...
Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf ...
In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task type...
Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle...
Accurate gene prediction remains a major bottleneck in fungal genomics, where lineage diversity and alternative splicing challenge existing ab initio ...
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational an...
While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardio...
While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification,...
Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in antibiotic prescribing remains uncertain, particular...