69 research outputs found
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search
Cascading multiple pre-trained models is an effective way to compose an
end-to-end system. However, fine-tuning the full cascaded model is parameter
and memory inefficient and our observations reveal that only applying adapter
modules on cascaded model can not achieve considerable performance as
fine-tuning. We propose an automatic and effective adaptive learning method to
optimize end-to-end cascaded multi-task models based on Neural Architecture
Search (NAS) framework. The candidate adaptive operations on each specific
module consist of frozen, inserting an adapter and fine-tuning. We further add
a penalty item on the loss to limit the learned structure which takes the
amount of trainable parameters into account. The penalty item successfully
restrict the searched architecture and the proposed approach is able to search
similar tuning scheme with hand-craft, compressing the optimizing parameters to
8.7% corresponding to full fine-tuning on SLURP with an even better
performance
FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
Due to the recent success of diffusion models, text-to-image generation is
becoming increasingly popular and achieves a wide range of applications. Among
them, text-to-image editing, or continuous text-to-image generation, attracts
lots of attention and can potentially improve the quality of generated images.
It's common to see that users may want to slightly edit the generated image by
making minor modifications to their input textual descriptions for several
rounds of diffusion inference. However, such an image editing process suffers
from the low inference efficiency of many existing diffusion models even using
GPU accelerators. To solve this problem, we introduce Fast Image Semantically
Edit (FISEdit), a cached-enabled sparse diffusion model inference engine for
efficient text-to-image editing. The key intuition behind our approach is to
utilize the semantic mapping between the minor modifications on the input text
and the affected regions on the output image. For each text editing step,
FISEdit can automatically identify the affected image regions and utilize the
cached unchanged regions' feature map to accelerate the inference process.
Extensive empirical results show that FISEdit can be and
faster than existing methods on NVIDIA TITAN RTX and A100 GPUs
respectively, and even generates more satisfactory images.Comment: 12 pages, 7 figure
ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs
The integration of Computer-Assisted Diagnosis (CAD) with Large Language
Models (LLMs) holds great potential in clinical applications, specifically in
the roles of digital family doctors and clinic assistants. However, current
works in this field are plagued by limitations, specifically a restricted scope
of applicable image domains and the provision of unreliable medical advice This
restricts their overall processing capabilities. Furthermore, the mismatch in
writing style between LLMs and radiologists undermines their practical
usefulness. To tackle these challenges, we introduce ChatCAD+, which is
designed to be universal and reliable. It is capable of handling medical images
from diverse domains and leveraging up-to-date information from reputable
medical websites to provide reliable medical advice. Additionally, it
incorporates a template retrieval system that improves report generation
performance via exemplar reports, enabling seamless integration into existing
clinical workflows. The source code is available at
https://github.com/zhaozh10/ChatCAD.Comment: Authors Zihao Zhao, Sheng Wang, Jinchen Gu, Yitao Zhu contributed
equally to this work and should be considered co-first author
High temperature superconductivity of quaternary hydrides XM3Be4H32 (X, M = Ca, Sr, Ba, Y, La, Ac, Th) under moderate pressure
The compressed hydrogen-rich compounds have received extensive attention as
promising candidates for room temperature superconductivity, however, the high
pressure required to stabilize such materials hinders their wide practical
application. In order to search for potential superconducting hydrides that are
stable at low pressures, we have investigated the crystal structures and
properties of quaternary hydrides, XM3Be4H32 (X, M = Ca, Sr, Ba, Y, La, Ac, Th)
based on the first-principles calculations. We identified nine dynamically
stable compounds at moderate pressure of 20 GPa. Strikingly, their
superconducting transition temperatures are much higher than that of liquid
nitrogen, especially CaTh3Be4H32 (124 K at 5 GPa), ThLa3Be4H32(134 K at 10
GPa), LaAc3Be4H32 (135 K at 20 GPa) and AcLa3Be4H32 (153 K at 20 GPa) exhibit
outstanding superconductivity at mild pressures. Metal atoms acting as
pre-compressors donate abundant electrons to hydrogen, weakening the H-H
covalent bond and thus facilitating the metallization of the hydrogen
sublattice. At the same time, the appropriate combination of metal elements
with different ionic radius and electronegativity can effectively tune the
electronic structure near the Fermi level and improve the superconductivity.
These findings fully reveal the great promise of hosting high-temperature
superconductivity of quaternary hydrides at moderate pressures and will further
promote related exploration.Comment: 14 pages, 6 figure
Protective Effect of Anthocyanin on Neurovascular Unit in Cerebral Ischemia/Reperfusion Injury in Rats
Treating cerebral ischemia continues to be a clinical challenge. Studies have shown that the neurovascular unit (NVU), as the central structural basis, plays a key role in cerebral ischemia. Here, we report that anthocyanin, a safe and natural antioxidant, could inhibit apoptosis and inflammation to protect NVU in rats impaired by middle cerebral artery occlusion/reperfusion (MCAO/R). Administration of anthocyanin significantly reduced infarct volume and neurological scores in MCAO/R rats. Anthocyanin could also markedly ameliorate cerebral edema and reduce the concentration of Evans blue (EB) by inhibiting MMP-9. Moreover, anthocyanin alleviated apoptotic injury resulting from MCAO/R through the regulation of Bcl-2 family proteins. The levels of inflammation-related molecules including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6), which were over-expressed with MCAO/R, were decreased by anthocyanin. In addition, Nuclear factor-kappa B (NF-κB) and the NLRP3 inflammasome pathway might be involved in the anti-inflammatory effect of anthocyanin. In conclusion, anthocyanin could protect the NVU through multiple pathways, and play a protective role in cerebral ischemia/reperfusion injury
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