Flow-based generative modeling stands out in computational science as a sophisticated approach that facilitates rapid and accurate inferences for complex, high-dimensional datasets. It is particularly relevant in domains requiring efficient inverse problem-solving, such as astrophysics, particle physics, and dynamical system predictions. In these fields, researchers work to understand and interpret complex data by developing models… →
BACKGROUND: Professional guidelines recommend HCC screening in at-risk patients using semi-annual ultrasound with or without alpha-fetoprotein (AFP); however, this strategy has limited effectiveness due to low adherence and sensitivity. Increasing data support the potential role of blood-based biomarker panels, which could improve both aspects. The biomarker panel GALAD, comprised of sex, age, and 3 blood… →
INTRODUCTION: Generalized anxiety disorder (GAD) is a common mental disorder that often begins in adolescence or early adulthood and is characterized by widespread and persistent anxiety. Partial sleep deprivation (PSD) is an important risk factor for GAD development and a common comorbidity. Adolescence is a period of rapid brain and nervous system development, and during… →
CONCLUSION: Whole-lesion histogram parameters derived from sCT IM analysis, and especially the 99^(th) percentile, can serve as imaging biomarkers of HER2 overexpression in GC. →
Foundation models hold promise in medicine, especially in assisting complex tasks like Medical Decision-Making (MDM). MDM is a nuanced process requiring clinicians to analyze diverse data sources—like imaging, electronic health records, and genetic information—while adapting to new medical research. LLMs could support MDM by synthesizing clinical data and enabling probabilistic and causal reasoning. However, applying… →
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical challenge in this field is understanding and predicting the relationship between the number of demonstrations provided and the model’s performance improvement, known as the ICL curve. This relationship needs to be… →
Large language models (LLMs) are getting better at scaling and handling long contexts. As they are being used on a large scale, there has been a growing demand for efficient support of high-throughput inference. However, efficiently serving these long-context LLMs presents challenges related to the key-value (KV) cache, which stores previous key-value activations to avoid… →
Recent advancements in generative language modeling have propelled natural language processing, making it possible to create contextually rich and coherent text across various applications. Autoregressive (AR) models generate text in a left-to-right sequence and are widely used for tasks like coding and complex reasoning. However, these models face limitations due to their sequential nature, which… →
Creating a common semantic space where queries and items can be represented as dense vectors is the main goal of embedding-based retrieval. Instead of depending on precise keyword matches, this method enables effective matching based on semantic similarities. Semantically related things are positioned closer to one another in this common area since searches and items… →