Artificial Intelligence (AI) is revolutionizing how discoveries are made. AI is creating a new scientific paradigm with the acceleration of processes like data analysis, computation, and idea generation. Researchers want to create a system that eventually learns to bypass humans completely by completing the research cycle without human involvement. Such developments could raise productivity and… →
GANs are often criticized for being difficult to train, with their architectures relying heavily on empirical tricks. Despite their ability to generate high-quality images in a single forward pass, the original minimax objective is challenging to optimize, leading to instability and risks of mode collapse. While alternative objectives have been introduced, issues with fragile losses… →
The objective of this study was to evaluate the therapeutic effects of Chiglitazar combined with Rosa roxburghii Tratt (RRT) in inpatients diagnosed with psychiatric disorders and antipsychotic-induced metabolic syndrome (MetS).100 cases were included and divided into the Siglitazar group (n=50) and the Siglitazar + RRT group (n=50) Anthropometric measurements, lipid and glucose metabolism indicators, inflammatory… →
BACKGROUND: Compared to older adults with breast cancer (BC), adolescents and young adults (AYAs) develop more aggressive disease necessitating more intensive therapy with curative intent, which is disruptive to planned life trajectories. The burden of unmet needs among AYA BC survivors exists in two domains: (1) symptoms (e.g., sexual problems, anxiety, fatigue, stress, hot flashes)… →
CONCLUSION: School dental screening programs with referral to specific dental hospital had a highly significant impact in reducing decayed teeth and increasing the number of FT 27 in children. →
Autoregressive pre-training has proved to be revolutionary in machine learning, especially concerning sequential data processing. Predictive modeling of the following sequence elements has been highly effective in natural language processing and, increasingly, has been explored within computer vision domains. Video modeling is one area that has hardly been explored, giving opportunities for extending into action… →
Large language models (LLMs) like GPT-4, Bard, and Copilot have made a huge impact in natural language processing (NLP). They can generate text, solve problems, and carry out conversations with remarkable accuracy. However, they also come with significant challenges. These models require vast computational resources, making them expensive to train and deploy. This excludes smaller… →
Multi-modal Large Language Models (MLLMs) have revolutionized various image and video-related tasks, including visual question answering, narrative generation, and interactive editing. A critical challenge in this field is achieving fine-grained video content understanding, which involves pixel-level segmentation, tracking with language descriptions, and performing visual question answering on specific video prompts. While state-of-the-art video perception models… →
Large Language Models (LLMs) have revolutionized generative AI, showing remarkable capabilities in producing human-like responses. However, these models face a critical challenge known as hallucination, the tendency to generate incorrect or irrelevant information. This issue poses significant risks in high-stakes applications such as medical evaluations, insurance claim processing, and autonomous decision-making systems where accuracy is… →
Understanding and processing human language has always been a difficult challenge in artificial intelligence. Early AI systems often struggled to handle tasks like translating languages, generating meaningful text, or answering questions accurately. These systems relied on rigid rules or basic statistical methods that couldn’t capture the nuances of context, grammar, or cultural meaning. As a… →