8,845 research outputs found
Macrophage migration inhibitory factor (MIF) family in arthropods : Cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain
Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Peer reviewedPostprin
Evolution of topological charge through chiral anomaly transport
Built upon the state-of-the-art model a multiphase transport (AMPT), we
develop a new module of chiral anomaly transport (CAT), which can trace the
evolution of the initial topological charge of gauge field created through
sphaleron transition at finite temperature and external magnetic field in heavy
ion collisions. The eventual experimental signals of chiral magnetic
effect(CME) can be measured. The CAT explicitly shows the generation and
evolution of the charge separation, and the signals of CME through the CAT are
quantitatively in agreement with the experimental measurements in Au+Au
collision at , and the centrality dependence of the CME
fraction follows that of the fireball temperature.Comment: 7 pages, 6 figure
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Diffusion models, which have emerged to become popular text-to-image
generation models, can produce high-quality and content-rich images guided by
textual prompts. However, there are limitations to semantic understanding and
commonsense reasoning in existing models when the input prompts are concise
narrative, resulting in low-quality image generation. To improve the capacities
for narrative prompts, we propose a simple-yet-effective parameter-efficient
fine-tuning approach called the Semantic Understanding and Reasoning adapter
(SUR-adapter) for pre-trained diffusion models. To reach this goal, we first
collect and annotate a new dataset SURD which consists of more than 57,000
semantically corrected multi-modal samples. Each sample contains a simple
narrative prompt, a complex keyword-based prompt, and a high-quality image.
Then, we align the semantic representation of narrative prompts to the complex
prompts and transfer knowledge of large language models (LLMs) to our
SUR-adapter via knowledge distillation so that it can acquire the powerful
semantic understanding and reasoning capabilities to build a high-quality
textual semantic representation for text-to-image generation. We conduct
experiments by integrating multiple LLMs and popular pre-trained diffusion
models to show the effectiveness of our approach in enabling diffusion models
to understand and reason concise natural language without image quality
degradation. Our approach can make text-to-image diffusion models easier to use
with better user experience, which demonstrates our approach has the potential
for further advancing the development of user-friendly text-to-image generation
models by bridging the semantic gap between simple narrative prompts and
complex keyword-based prompts. The code is released at
https://github.com/Qrange-group/SUR-adapter.Comment: accepted by ACM MM 202
- …